{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Discontinuity Design\n",
    "\n",
    "## Regression Discontinuity Design\n",
    "\n",
    "We don't stop to think about it much, but it is impressive how smooth nature is. You can't grow a tree without first getting a bud, you can't teleport from one place to another, a wound takes its time to heal. Even in the social realm, smoothness seems to be the norm. You can't grow a business in one day, consistency and hard work are required to build wealth and it takes years before you learn how linear regression works. Under normal circumstances, nature is very cohesive and doesn't jump around much.\n",
    "\n",
    "\n",
    "> When the intelligent and animal souls are held together in one embrace, they can be kept from separating.\n",
    "\n",
    "\\- Tao Te Ching, Lao Tzu.\n",
    "\n",
    "Which means that **when we do see jumps and spikes, they are probably artificial** and often man made situations. These events are usually accompanied by counterfactuals to the normal way of things: if a weird thing happens, this gives us some insight into what would have happened if nature was to work in a different way. Exploring these artificial jumps is at the core of Regression Discontinuity Design.\n",
    "\n",
    "![img](./data/img/rdd/smooth.png)\n",
    "\n",
    "The basic setup goes like this. Imagine that you have a treatment variable \\\\(T\\\\) and potential outcomes \\\\(Y_0\\\\) and \\\\(Y_1\\\\). The treatment T is a discontinuous function of an observed running variable \\\\(R\\\\) such that\n",
    "\n",
    "$\n",
    "D_i = \\mathcal{1}\\{R_i>c\\}\n",
    "$\n",
    "\n",
    "In other words, this is saying that treatment is zero when \\\\(R\\\\) is below a threshold \\\\(c\\\\) and one otherwise. This means that we get to observe \\\\(Y_1\\\\) when \\\\(R>c\\\\) and \\\\(Y_0\\\\) when \\\\(R<c\\\\). To wrap our head around this, think about the potential outcomes as 2 functions that we can't observe entirely. Both \\\\(Y_0(R)\\\\) and \\\\(Y_1(R)\\\\) are there, we just can't see that. The threshold acts as a switch that allows us to see one or the other of those function, but never both, much like in the image below:\n",
    "\n",
    "![img](./data/img/rdd/rdd.png)\n",
    "\n",
    "The idea of regression discontinuity is to compare the outcome just above and just below the threshold to identify the treatment effect at the threshold. This is called a **sharp RD** design, since the probability of getting the treatment jumps from 0 to 1 at the threshold, but we could also think about a **fuzzy RD** design, where the probability also jumps, but is a less dramatic manner.\n",
    "\n",
    "## Is Alcohol Killing You?\n",
    "\n",
    "A very relevant public policy question is what should be the minimal drinking age. Most countries, Brazil included, set it to be 18 year, but in the US (most states) it is currently 21. So, is it the case that the US is being overly prudent and that they should lower their minimal drinking age? Or is it the case that other countries should make their legal drinking age higher? \n",
    "\n",
    "One way to look at this question is from a [mortality rate perspective (Carpenter and Dobkin, 2009)](https://www.aeaweb.org/articles?id=10.1257/app.1.1.164). From the public policy standpoint, one could argue that we should lower the mortality rate as much as possible. If alcohol consumption increases the mortality rate by a lot, we should avoid lowering the minimum drinking age. This would be consistent with the objective of lowering deaths caused by alcohol consumption.\n",
    "\n",
    "To estimate the impacts of alcohol, we could use the fact that legal drinking age imposes a discontinuity on nature. In the US, those just under 21 years don't drink (or drink much less) while those just older than 21 do drink. This means that the probability of drinking jumps at 21 years and that is something we can explore with an RDD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import style\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "style.use(\"fivethirtyeight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do so we can grab some mortality data aggregated data by age. Each row is a has the average age of a group of people and the average mortality by all causes (`all`), by moving veiche accident (`mva`) and by suicide (`suicide`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agecell</th>\n",
       "      <th>all</th>\n",
       "      <th>mva</th>\n",
       "      <th>suicide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.068493</td>\n",
       "      <td>92.825400</td>\n",
       "      <td>35.829327</td>\n",
       "      <td>11.203714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.150684</td>\n",
       "      <td>95.100740</td>\n",
       "      <td>35.639256</td>\n",
       "      <td>12.193368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.232876</td>\n",
       "      <td>92.144295</td>\n",
       "      <td>34.205650</td>\n",
       "      <td>11.715812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.315070</td>\n",
       "      <td>88.427760</td>\n",
       "      <td>32.278957</td>\n",
       "      <td>11.275010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.397260</td>\n",
       "      <td>88.704940</td>\n",
       "      <td>32.650967</td>\n",
       "      <td>10.984314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     agecell        all        mva    suicide\n",
       "0  19.068493  92.825400  35.829327  11.203714\n",
       "1  19.150684  95.100740  35.639256  12.193368\n",
       "2  19.232876  92.144295  34.205650  11.715812\n",
       "3  19.315070  88.427760  32.278957  11.275010\n",
       "4  19.397260  88.704940  32.650967  10.984314"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinking = pd.read_csv(\"./data/drinking.csv\")\n",
    "drinking.head()[[\"agecell\", \"all\", \"mva\", \"suicide\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to aid visibility, and for another important reason we will see later, we will centralize the running variable `agecell` at the threshold 21."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinking[\"agecell\"] -= 21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot the multiple outcome variables (`all`, `mva`, `suicide`) with the runing variable on the x axis, we get some visual cue that there is also a jump in mortality as we cross the legal drinking age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "ax = plt.subplot(3,1,1)\n",
    "drinking.plot.scatter(x=\"agecell\", y=\"all\", ax=ax)\n",
    "plt.title(\"Death Cause by Age (Centered at 0)\")\n",
    "\n",
    "ax = plt.subplot(3,1,2, sharex=ax)\n",
    "drinking.plot.scatter(x=\"agecell\", y=\"mva\", ax=ax)\n",
    "\n",
    "ax = plt.subplot(3,1,3, sharex=ax)\n",
    "drinking.plot.scatter(x=\"agecell\", y=\"suicide\", ax=ax);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some cues, but we need more than that. What exactly is the effect of drinking on mortality at the threshold? And what is the standard error on that estimate?\n",
    "\n",
    "## RDD Estimation\n",
    "\n",
    "The key assumption that RDD relies on is the smoothness of the potential outcome at the threshold. Formally, the limits of the potential outcomes as the running variable approaches the threshold from the right and from the left should be the same.\n",
    "\n",
    "$$\n",
    "\\lim_{r \\to c^-} E[Y_{ti}|R_i=r] = \\lim_{r \\to c^+} E[Y_{ti}|R_i=r]\n",
    "$$\n",
    "\n",
    "If this holds true, we can find the causal effect at the threshold\n",
    "\n",
    "\\begin{align}\n",
    "\\lim_{r \\to c^+} E[Y_{ti}|R_i=r] - \\lim_{r \\to c^-} E[Y_{ti}|R_i=r]=&\\lim_{r \\to c^+} E[Y_{1i}|R_i=r] - \\lim_{r \\to c^-} E[Y_{0i}|R_i=r] \\\\\n",
    "=& E[Y_{1i}|R_i=r] - E[Y_{0i}|R_i=r] \\\\\n",
    "=& E[Y_{1i} - Y_{0i}|R_i=r]\n",
    "\\end{align}\n",
    "\n",
    "This is, in its own way, a sort of Local Average Treatment Effect (LATE), since we can only know it at the threshold. In this setting, we can think of RDD as a local randomized trial. For those at the threshold, the treatment could have gone either way and, by chance, some people fell below the threshold, and some people fell above. In our example, at the same point in time, some people are just above 21 years and some people are just below 21. What determines this is if someone was born some days later or not, which is pretty random. For this reason, RDD provides a very compelling causal story. It is not the golden standard of RCT, but it is close. \n",
    "\n",
    "Now, to estimate the treatment effect at the threshold, all we need to do is estimate both of the limits in the formula above and compare them. The simplest way to do that is running a linear regression\n",
    "\n",
    "![img](./data/img/rdd/ols.png)\n",
    "\n",
    "To make it work, we interact a dummy for being above the threshold with the running variable\n",
    "\n",
    "$\n",
    "y_i = \\beta_0 + \\beta_1 r_i + \\beta_2 \\mathcal{1}\\{r_i>c\\} + \\beta_3 \\mathcal{1}\\{r_i>c\\} r_i\n",
    "$\n",
    "\n",
    "What this is doing is fitting a linear regression above the threshold and another below it. The parameter \\\\(\\beta_0\\\\) is the intercept of the regression below the threshold and \\\\(\\beta_0+\\beta_2\\\\) is the intercept for the regression above the threshold. \n",
    "\n",
    "Here is where the trick of centering the running variable at the threshold comes in play. After this pre-processing step, the threshold becomes zero. This causes the intercept \\\\(\\beta_0\\\\) to be the predicted value at the threshold, for the regression below it. In other words, \\\\(\\beta_0=\\lim_{r \\to c^-} E[Y_{ti}|R_i=r]\\\\). By the same reasoning, \\\\(\\beta_0+\\beta_2\\\\) is the limit of the outcome from above. Wich means, that\n",
    "\n",
    "$\n",
    "\\lim_{r \\to c^+} E[Y_{ti}|R_i=r] - \\lim_{r \\to c^-} E[Y_{ti}|R_i=r]=\\beta_2=E[ATE|R=c]\n",
    "$\n",
    "\n",
    "Here is what this looks like in code for the case where we want to estimate the effect of alcohol consumption on death by all causes at 21 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   93.6184</td> <td>    0.932</td> <td>  100.399</td> <td> 0.000</td> <td>   91.739</td> <td>   95.498</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>agecell</th>           <td>    0.8270</td> <td>    0.819</td> <td>    1.010</td> <td> 0.318</td> <td>   -0.823</td> <td>    2.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>threshold</th>         <td>    7.6627</td> <td>    1.319</td> <td>    5.811</td> <td> 0.000</td> <td>    5.005</td> <td>   10.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>agecell:threshold</th> <td>   -3.6034</td> <td>    1.158</td> <td>   -3.111</td> <td> 0.003</td> <td>   -5.937</td> <td>   -1.269</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdd_df = drinking.assign(threshold=(drinking[\"agecell\"] > 0).astype(int))\n",
    "\n",
    "model = smf.wls(\"all~agecell*threshold\", rdd_df).fit()\n",
    "\n",
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model is telling us that mortality increases by 7.6627 points with the consumption of alcohol. Another way of putting this is that alcohol increases the chance of death by all causes by 8% ((7.6627+93.6184)/93.6184). Notice that this also gives us standard errors for our causal effect estimate. In this case, the effect is statistically significant, since the p-value is below 0.01.\n",
    "\n",
    "If we want to verify this model visually, we can show the predicted values on the data that we have. You can see that it is as though we have 2 regression models: one for those above the threshold and one for below it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = drinking.plot.scatter(x=\"agecell\", y=\"all\", color=\"C0\")\n",
    "drinking.assign(predictions=model.fittedvalues).plot(x=\"agecell\", y=\"predictions\", ax=ax, color=\"C1\")\n",
    "plt.title(\"Regression Discontinuity\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we do the same for the other causes, this is what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for p, cause in enumerate([\"all\", \"mva\", \"suicide\"], 1):\n",
    "    ax = plt.subplot(3,1,p)\n",
    "    drinking.plot.scatter(x=\"agecell\", y=cause, ax=ax)\n",
    "    m = smf.wls(f\"{cause}~agecell*threshold\", rdd_df).fit()\n",
    "    ate_pct = 100*((m.params[\"threshold\"] + m.params[\"Intercept\"])/m.params[\"Intercept\"] - 1)\n",
    "    drinking.assign(predictions=m.fittedvalues).plot(x=\"agecell\", y=\"predictions\", ax=ax, color=\"C1\")\n",
    "    plt.title(f\"Impact of Alcohol on Death: {np.round(ate_pct, 2)}%\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RDD is telling us that alcohol increases the chance of deth by suicide and car accidents by 15%, wich is a pretty significant ammount. These results are compelling arguments to not lower the drinking age, if we want to minimize mortality rates.\n",
    "\n",
    "### Kernel Weighting\n",
    "\n",
    "Regression Discontinuity relies heavily on the extrapolations properties of linear regression. Since we are looking at the values at the beginning and end of 2 regression lines, we better get those limits right. What can happen is that regression might focus too much on fitting the other data points at the cost of a poor fit at the threshold. If this happens, we might get the wrong measure of the treatment effect.\n",
    "\n",
    "One way to solve this is to give higher weights for the points that are closer to the threshold. There are many ways to do this, but a popular one is to reweight the samples through the **triangular kernel**\n",
    "\n",
    "$\n",
    "K(R, c, h) = \\mathcal{1}\\{|R-c| \\leq h\\} * \\bigg(1-\\frac{|R-c|}{h}\\bigg)\n",
    "$\n",
    "\n",
    "The first part of this kernel is an indicator function to whether we are close to the threshold. How close? This is determined by a bandwidth parameter \\\\(h\\\\). The second part of this kernel is a weighting function. As we move away from the threshold, the weights get smaller and smaller. These weights are divided by the bandwidth. If the bandwidth is large, the weights get smaller at a slow rate. If the bandwidth is small, the weights quickly go to zero. \n",
    "\n",
    "To make it easier to understand, here is what the weights look like for this kernel applied to our problem. I've set the bandwidth to be 1 here, meaning we will only consider data from people that are no older than 22 years and no younger than 20 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kernel(R, c, h):\n",
    "    indicator = (np.abs(R-c) <= h).astype(float)\n",
    "    return indicator * (1 - np.abs(R-c)/h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(drinking[\"agecell\"], kernel(drinking[\"agecell\"], c=0, h=1))\n",
    "plt.xlabel(\"agecell\")\n",
    "plt.ylabel(\"Weight\")\n",
    "plt.title(\"Kernel Weight by Age\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we apply these weights to our original problem, the impact of alcohol gets bigger, at least for all causes. It jumps from 7.6627 to 9.7004. The result remains very significant. Also, notice that I'm using `wls` instead of `ols`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   93.2002</td> <td>    0.731</td> <td>  127.429</td> <td> 0.000</td> <td>   91.726</td> <td>   94.674</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>agecell</th>           <td>    0.4109</td> <td>    1.789</td> <td>    0.230</td> <td> 0.819</td> <td>   -3.196</td> <td>    4.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>threshold</th>         <td>    9.7004</td> <td>    1.034</td> <td>    9.378</td> <td> 0.000</td> <td>    7.616</td> <td>   11.785</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>agecell:threshold</th> <td>   -7.1759</td> <td>    2.531</td> <td>   -2.835</td> <td> 0.007</td> <td>  -12.276</td> <td>   -2.075</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.wls(\"all~agecell*threshold\", rdd_df,\n",
    "                weights=kernel(drinking[\"agecell\"], c=0, h=1)).fit()\n",
    "\n",
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = drinking.plot.scatter(x=\"agecell\", y=\"all\", color=\"C0\")\n",
    "drinking.assign(predictions=model.fittedvalues).plot(x=\"agecell\", y=\"predictions\", ax=ax, color=\"C1\")\n",
    "plt.title(\"Regression Discontinuity (Local Regression)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here is what it looks like for the other causes of death. Notice how the regression on the right is more negatively sloped since it disconsiders the right most points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pUn+O7JZWDUtaNTTPGzduHPHx8cybN8/qDrm2tHHjRlasWMGWLVtwdHRk/vz5FC5cmO+++44CBQrg5+fHli1bmD9/Pj/++GO258/eSAAjstTdu3ctbo+MjASe/TjpdDpmzZqFm5sbwcHByZ4cLTXRJP1g3b59O8sm5kvKX0rliIiIMEuXUTqdjiVLlgDw2Wefpdip9Y8//uCDDz5Itd086X1JulmmJjvKl13nePDgAU+ePLEYxNjqOmVWUofU+vXrm7Yl5Wnnzp1WPXVn5Ltiz3x9fQG4fPmyxdeTtlvTHHT8+HFiY2OpXbu2xddXrlzJypUrqVGjBnv27Mlgji2Liopi3LhxjBgxwnT9z58/j4+Pj+kzW6BAAXx8fDh37pxNz51XSQAjstSBAwcwGAzJmjL27t0LQK1atYDE6u7o6GiaNWuW7Af54cOHFpsX6tevz7Fjx9i2bVuawyAz+kSd9EO3e/duizNvJo3MyGx178aNG7lz5w4VKlQwTYT2ooMHD3LhwgX+/fdfunbtmuKxGjRoAMCOHTssvvfPe758liT1u8hM+SpVqkTBggUJDQ3l3r17yZqRbHGO2rVrs3PnTvbs2UPr1q3NXgsNDSUyMhIfH59ktS/Z6dy5c6xZswaFQmHWHNegQQPWrl3L/v37rXoPMvJdyaoapeyQNJpu27ZtyUbsXLt2jUuXLuHp6Zlm0yrAK6+8Ytb/KElERARbtmyhfPnyNG3a1Cb951704YcfUqBAAT766CPTNoVCkaymLD4+XuaEsZJ04hVZ6vLlyyxcuNBs25o1awgJCcHX1xd/f38gsUmpYMGCHD9+nIcPH5rSarVaJk6caPFHZ8iQIajVar744gtCQ0OTvX7r1i3Tv4sXLw48G5lkrY4dO1K8eHHWrFljCrqSLF26lGPHjlG1alVT0JBRSR10J0yYwNy5cy3+9+GHHwIpd0ZNUqdOHfz9/QkNDeWLL75I9np0dLTpPfb396dy5cocOXKEv/76yyxdcHAw69ato0SJEmbzb6SXRqOhd+/ePH78mGnTppnN1xEeHs7XX3+NQqHgtddey/A5Xn/9dQCmT5+e7POT9L4NGDAgw8fPrODgYHr27ElCQgJDhw6lRo0aptdee+01XFxcmD17tsV+Jkajkf3795vmpcnIdyWjn/+0XL161dTHKas0adKEypUrs2/fPrPO5AaDwdScOnjwYLMmIa1Wy4ULF7hw4YLZsaZOnWrxu5XUV6Vu3brMnTs31UntMmL79u0sW7bM1FSUpEqVKpw7d840rcH169c5d+5css7cwjKpgRFZKjAwkA8//JBt27ZRvXp1Ll++zLp16yhQoABz5841/egolUreeustvv76a9OEVVqtlt27d/PgwQOaNm2arJagSpUqfPnll4wdO5aAgADatWuHr68vDx484OTJk8THx5v28fX1xcPDg/379zN06FC8vb1RqVS0b9/e7GbyImdnZ+bPn8+AAQPo1q0bXbp0oXz58pw+fZotW7ZQtGhRFixYkKn29LCwMIKCgnBxcUl1mHSHDh1wdXUlKCiIsLCwZB2qn/fDDz/QqVMnPv30UzZs2GDq3Hr16lV27NjB5s2bqVWrFgqFggULFtCtWzeGDx/OqlWrqF69OlevXmXt2rU4ODjw/fffJxtCnV5Tpkxh//79LFq0iJMnTxIQEEB0dDSrV6/mwYMHvP/++2bNKun1yiuvsGnTJv7++28aNmxIx44d0Wg0bNq0iUuXLtG8eXNGjhyZqTJYY8+ePaaOuPHx8URERHDo0CEuXbqESqVizJgxTJ061WyfYsWKsWjRIl577TXatGlDs2bNqFKlChqNhlu3bnH48GFu3rzJtWvXcHBwyNB3JaOf/7R06dKFGzdusG7dOosdqC1Zv369afLCpGbFw4cPm032mDSHEyTWHs2bN48uXbowcOBAunbtioeHB8HBwRw7doyGDRsmu7a3b982daq3RUfs9Ob5ebGxsbzzzjsMGTKExo0bm702cuRI/v77b7p06UL79u3ZuHEjRqORt99+O9N5zg8kgBFZqn79+owfP55PPvnE1CmtRYsWTJ48OVl1+YcffkiJEiVYvHgxv/32G0WKFCEgIICPPvooxZWPBw4cSLVq1Zg7dy4HDhxg48aNFC9enMqVK/Pmm2+a0qlUKpYsWcLUqVPZvHkzsbGxGI1G3N3d0/wBb9euHVu2bOGrr74iODiYNWvW4OrqSt++fXn//fetqrpOTVLn3VdffRUnJ6cU02k0Gvr27cucOXNYtGgRkydPTjFt+fLl2bVrF3PnzmX9+vX89NNPODo64uHhwdChQylXrpwpbd26ddm5cyezZ89m586dbN++naJFi9KxY0f+97//mZr5MsPFxYXNmzfz7bffmqZ+d3R0pFatWrz11lt06dIl0+f44YcfaNy4MYsXL2bx4sUYDAa8vb2ZPn06w4cPN3VGz0p79+41zQlUsGBBXFxcqFSpEr169aJ3794pflaaNWvG3r17+e6779i+fTshISGo1Wrc3Nzw8/Nj6tSpZv130vtdyczn39ZOnTqVbI2w69evm02u+GIwUL9+fXbs2MGsWbMICgoiNjYWT09P3n//fcaOHZuhdZCyOs9J/u///g+lUmmx833S8gKffPIJP//8M+XLl+fXX3+1yXcuP1BERUWlb/5lIaywdOlSRo0axYQJE9I9mZsQQgiRFukDI4QQQgi7IwGMEEIIIeyOBDBCCCGEsDvSB0YIIYQQdkdqYIQQQghhdySAEUIIIYTdkQBGCCGEEHZHAhgrXbx4MaezkC2knHmLlDNvkXLmLVLOzJGZeIUQecq1GC3DdkXxX7yeko4qfmruglfhlFfvFkLYJ6mBEULkKcN2RRESmcCVGD0hkQkMDY7K6SwJIbKABDBCiDzlv3h9qn8LIfIGCWCEEHlKSUdVqn8LIfIG6QMjhMhTfmruwtBg8z4wIvd79OgROp0OACcnJ6Kjo3M4R1lPygnOzs4ZXileAhghRJ7iVVjDlk6uOZ0NkQ7x8fEAFC1aFABHR0ecnJxyMkvZIr+X02g0EhUVReHChTMUxEgTkhBCiBwVFxdHwYIFczobIpspFApcXFx49OhRhvaXAEYIIUSOUygUOZ0FkQMyc90lgBFCCCGE3ZEARgghhBB2RwIYIYQQIpfr3bs3I0aMMP3dsWNHxo8fn6ljjhgxgt69e2c2azlGRiEJIYQQdmbJkiVWj9zZvXs3nTt35vLly5QoUcK0fdasWRiNxqzKYpaTAEYIIYTIBgkJCTg4ONjkWMWKFcv0MZKGrdsraUISQgghMqBjx46MHTuWCRMm4OXlhZeXF5MnT8ZgMABQs2ZNZs6cyahRoyhXrhxDhw4F4ODBg3To0IEKFSpQtWpVxo0bR0xMjOm4jx8/ZsSIEZQtWxZfX1++/PJLi+d+vgkpISGB6dOnU6NGDUqVKkXt2rX5/vvvCQsLo3PnzgB4e3vj4uJiaop6sQkpPj6eiRMn4uvri5ubG61atWL//v2m13fv3o2LiwvBwcEEBgZSpkwZAgICOH78uClNdHQ0w4YNw8fHBzc3N2rXrs2PP/5og3c7OamBEUIIkesUGhiQred7+PvODO33999/07dvX7Zu3cqZM2d45513cHNz4+233wZg/vz5vPfee+zcuROj0ciZM2fo0aMHEydO5IsvvuDx48dMmjSJt99+m0WLFgEwefJkdu7cyaJFiyhTpgyfffYZ+/bto1OnTinmY8SIEezfv5+ZM2dSu3Ztrl+/zq1bt/Dw8GDRokUMGDCAAwcOUKxYsRQnz/u///s/Vq9ezXfffUf58uWZN28ePXv25MiRI5QuXdqUbtq0aUydOpXSpUszceJEhg0bxsGDB1EoFMyYMYPQ0FD++usvXF1dCQsLIzw8PEPvbVokgBFCiEy4FqNl2C7zpQu8CmtyOlsim7i5ufH555+jUCioVKkSly5dYv78+aYApnHjxrzzzjum9G+99Rbdu3dn9OjRxMXF4eTkxJdffkmzZs2IjIykQIECLF68mO+++47AwEAA5s2bR7Vq1VLMw+XLl/nnn39YsWIFrVq1AqB8+fKm15Oam1xdXc36wDzv0aNH/PLLL8yZM4e2bdsC8PXXX7Nr1y5+/vlnPvroI1PaDz/8kGbNmgHw/vvv065dO27fvk3ZsmW5ceMGtWvXpl69egCUK1eOuLi4dL2n1pIARgiRJzn+/BnGEqXQNgzEWKZclp1n2K4oQiITALiCnqHBUbKUQT5Sv359s8nY/Pz8+OSTT0xNQi+99JJZ+hMnTnDlyhVWrVqF0WhEoVCYOtJevXqVAgUKkJCQgJ+fn2mfQoUKUb169RTzcPLkSZRKJU2bNs1wOa5evYpWq6Vhw4ambSqVCj8/P86dO2eW9vm8JNXMREZGUrZsWYYMGcLAgQM5fvw4LVq0oF27dtSvXz/D+UqNBDBCiDxHcT8S9Z5NKIxGHFb/jt6rErpGgej8W2AsXsqm5/ovXp/q3yJ/c3Z2NvvbYDAwYMAARo4cSXx8PI6OjqbXypQpw6VLl7I7i2l6cbZcjUaT7LWkIKx169acOnWKrVu3EhwcTO/evenUqRM//PCDzfMlAYwQIs9RhwSheG54qCrsAqqwCzj89T2GyrXQNgxE16A5FMr8KIySjiquoDf7O7+yZXNaRvukZLcjR46YalIADh06RJkyZShSpIjF9LVr1+bs2bNUrFjR1IT0vAoVKqDRaDh06JCpGejRo0eEhoaaNQs9r2bNmhgMBnbv3m1qQnpe0sgnvT7l4LpChQo4ODhw4MABKlSoYEofEhJCz549U30PXlSiRAn69OlDnz59aN26NUOGDGHOnDlmwZotyCgkIUSeow7ZaXG7wmhEde4ETr99hfOYHjh9NRH1/m0Q9zjD5/qpuQt+rg5ULKLCz9WBn5q7ZPhY9i6pOe1KjJ6QyASGBkfldJay3J07d5g4cSIXL15kzZo1zJkzh5EjR6aY/p133uHo0aOMHTuWU6dOceXKFTZt2sS7774LJDYXvf7660ydOpWgoCDOnj3L22+/bRrZZImPjw/du3dnzJgxrFmzhmvXrrFv3z7+/PNPADw9PVEoFGzevJn//vuPhw8fJjuGs7MzgwcPZurUqWzZsoXz588zbtw4IiMjefPNN61+Pz755BPWr1/P5cuXOX/+POvWrcPLy8vmwQtIDYwQIg968r/PUB8KRn1wB6qzx8xqY5Io9HrUJw6gPnEAo4MTurovo2sYiL5mA1BbX2vgVVgjfV6eyo/Nab169cJgMBAYGIhCoeD1119PNYCpUaMG//77LzNmzKB79+4YDAbKly9Px44dTWk+/vhjHj16xGuvvUaBAgUYNmwYjx+nHmR///33fPLJJ0ycOJF79+7h7u5uyoe7uzuTJk1ixowZjBkzhj59+rBgwYJkx5g2bRoAo0aNIjo6mlq1arFixQqzEUhpcXR0ZMaMGYSFheHo6EiDBg1Mo6tsTREVFWW/0/Blo4sXL+Lr65vT2chyUs68RcoJigf/oT4YhPrAdlRXz1lM8zyjc2F0DQLQNWyJvnJtUOaeiurcfj3brI80dWgG8HN1sCq4i46ONptUzVLTSm7UsWNHqlWrxuzZszO0v72UM7PSKueL199aUgMjhMjTjMVKom3XC227Xiju3ER9cAea/dtQhl+3mF7xKBbNznVodq7DUKwkOv+W6BoGYihfCV7ozCjM/dTchaHB5n1ghMgqEsAIIfINY2kPtF0HoO3yOsrrl1Dv34b64A6U9yMtplc++A+HTctx2LQcQ2lPdA1bZvmwbHsmzWkiO0kAI4TIfxQKDF6+JHj5kvDqWygvnkazfxvqQztRPIyxuIvyzg0cVv+epcOyZVI8+7Jhw4aczkK+JgGMECLfSR4oVMXrjVrEvzYG1ZnDiTUzR/egiLc8g2hWDcuWSfGEsJ4EMEKIfCfFQEGtRl+7IfraDYmPj0N9fB/q/dtRnTyIQq9LdpykYdmqcycwLv4WfY0G6Bq1QvdSY3AqaJbWmtqV/DiKR4iMkgBGCJHvWBUoODolduD1bwmPYlEf3pU4ksnaYdkvNUbXqJVpWLY1tSv5eVK85yeDE/mH0cJ3yVoSwAgh8p10BwrOhdE174iuecfEYdkhQYk1MykMy1YkxKE5uAPNwR2Jw7LrN6ei9iUOaSphVCQOy7YUNOXXUTxOTk48fvw42bT7Im8zGo1ERUVRuHDhDO2fowHM3r17mTt3LidOnCA8PJx58+bRv39/0+tGo5FZs2bx+++/ExUVRb169fjiiy+oWrWqKU1UVBTvv/8+mzZtAqBdu3Z8/vnnuLi4ZHdxhBB2IjOBgrFYSbRte6Ftm45h2cHrWcJ6PnMoxvJSDVnm9jLqkpWSpc2vo3gcHR3R6XRER0cDEBMTk+JU/HmJlBMKFy6MWp2xUCRHA5hHjx5RrVo1+vbty/Dhw5O9/u233zJv3jzmzZuHr68vn3/+Od27d+fQoUOmiO3NN9/k5s2brFixAoAxY8bw1ltv8ddff2VrWYQQ9sNWgUKyYdkHtqM+sAPl/bsW05dNeMDYmxsZe3Mj8dfLotC1kmHZTz1f+3L37l08PT1zMDfZQ8qZOTkawLRp04Y2bdoAJJt62Wg0smDBAt599126du0KwIIFC/D19WXFihUMGjSI8+fPs23bNjZt2mRaevzrr7+mffv2uX7GSiFEHvL8sOxewxKHZR/YnrioZArDsh0jb8Hzw7IbtkTXsKXNV8sWIq/KPXNkvyAsLIyIiAhatmxp2lagQAEaN27MwYMHAQgJCaFQoUL4+/ub0jRs2BBnZ2dTGiGEyFZKJYbKtYgfOJZH367kybhZaBu3xuiY8lTqqrALOP71PQXH9abAp++gDloLD6OzMdNC2J9c24k3IiICAFdX82peV1dXwsPDgcRqqRIlSpj1XFcoFJQsWZK7dy1X4QohRLaxNCz7wHZUJ1IZln3+BKrzaQ/LFiK/y7UBTFa6ePFitu5nb6SceYuUMxcp7gkd3kDVohcu545S7EwIha6dR0Hqw7L1GgdifGtTpIY/l/Q6jKq8/9NtF9fTBqScKUurG0iu/Ra4ubkBEBkZadb5JzIyklKlEtuIS5Uqxb1798zmDzAajfz333+mNJZkpG9MfulTI+XMW6ScuVitOsBgHlsxLFulTaBY6CGKhR7C6FyYB7WbMlnjx/YilSjupMlzSw5cvHgRjVv5PL+sgl1+bjMgq8qZa/vAeHl54ebmRlBQkGlbXFwc+/fvN/V58fPz4+HDh4SEhJjShISE8OjRI7N+MUIIkVslDct+MvV7Hn22hPgegzGkMipJ8SiW4vv+ZV7wVLZvHkWv/b/wxeojkIkJwXKjpIn/rsToCYlMYGhwVE5nKU+4FqOlzfpI6v5zhzbrIwmL1eZ0ljIsR2tgHj58yJUrVwAwGAzcvHmTkydPUqxYMTw9PRkxYgRfffUVvr6++Pj48MUXX+Ds7EzPnj0BqFy5Mq1atWLs2LF88803AIwdO5a2bdvmi6hWCJG3ZHRYNjc3Yjjlga5hINqGLTG6e2Vzzm1PllXIGnlpva0cDWCOHTtG586dTX/PnDmTmTNn0rdvXxYsWMA777zDkydPGD9+vGkiu5UrV5rN2vfzzz/z/vvv88orrwDQvn17Pv/882wvixBC2EwKw7IV+7ejfvLQ4i7KiJs4rPkdhzW/o/fyRdcwEJ1/S4wlbDMsO7tXys7PyypkpbwUGOZoANO0aVOioqJSfF2hUDBp0iQmTZqUYhoXFxd+/PHHLMidEELkAknDsivX4qJ/O6rERxO/eysOR/dQUB9vcRdV2EVUYRdx/Ot79JVro23YMnG17MIuGc5Gdj+559dlFbJaXgoMc20nXiGEEC9QJQ7LVtduiCE+jrjj+1Ef2JbisGzg2bDsJXMSh2U3DERX9+V0D8vO7if3nFhWIbtrmXJCXgoMJYARQgh75OiEzr8FOv8WGVgt2xHdSy+jaxiIvpYfqNO+SeelJ/eU5KX+ISnJS+ttSQAjRDrkhyc0YYfSvVp2/AurZTdD16gV+sq1QGk5MMlLT+4pyUv9Q/IDCWCESIf88IQm7JvZatkRN1EfsGa17A1ogjdgcCmZWKvTKBBD+crw3CzneenJPSX5oZYpL5EARoh0kCc0YU+Mbukblq2M+g+HzX/jsPlvDG5l0TVslWeGZVvD2lomqYnNHSSAESId5AlN2KUMrJatjLiVpcOycyNra5mkJjZ3SDWAuXHjRoYO+vzU/0LkJfmhH4DI454blh3ffzSqM0dQH9iG+shuFPFxFnfJimHZ9kxqYnOHVAOYWrVqma30bK379+9nOENC5Gb5oR+AyEfUavS1/dHX9n+6WvbTYdknQ1DoLE8xb6th2fbMmppYa5uZ7Lk5KqfznmoA891332UogBFCCGEfzG9CNfhpcBO8lHEZGJbdGF3DVlYPy7Zn1tTEWtvMZM/NUTmd91QDmP79+2dXPoQQQuSAlG5CpmHZUfeeDcu+ctbiMRKHZQehORiEsWAhdA2apzks255ZUxNrbTOTPTdH5XTepROvEEKkIKeryLNDWjcho0sJtG16om3T89mw7APbUd4Os3g8xeOHzw3LLoHOvyW6hoEYKpgPy87rrO3wb8uBAfltvapUA5hly5Zl6KB9+/bN0H5CCJGb5HQVeXZIz03IbFj2jcuo929HfXAHynsRFtMro+5ZHJZ9tZB7ng8Mre3wb8uBAfltvapUA5iRI0em+4AKhUICGCFEnpDTVeTZIUM3IYUCQzkfEsr5kNBrKMpLpxNrZkKCUMRGW9zl+WHZGpcKNC3ekL9KNSLEqUSeDAyt7fBvy4EB+WG9quelGsCcOHEiu/IhhBC5Tk5XkWeHTN+ElEoMlWqRUKkWCf3efjosezvqo7tRxD2xuEvlqKt8FnWVz64sY3fRymz1agLNO+fbYdm2kh8+r89LNYApV65cduVD5GL5oR+AEJbkdBW53XlhWLbqxAE0+7ehOnkwxWHZTaPP0/TkeYzv/GYalq0sWjqbM5435LfPq3TiFWnKD/0AhLAkp6vI7ZqjE3q/APR+AYmrZR/ZnTgsO/QYCqMhWfLnh2XXVDugr/d0teyafqBxSPE08oD1TH77vKY7gLl79y6LFy/m+PHjxMTEYDCYfxAVCgVr1661WQZFzssP/QCEEFnIuTC6Zh3QNevwdFj2zsQJ8y5bHpat1CWgfH5YdtJq2VVqJxuWbc0DlgQ5eVO6ApjQ0FA6derE48eP8fHxITQ0lCpVqhAVFUV4eDgVKlSgbNmyWZVXkUNya7uq/CgJYX8Sh2W/grbNKygibqE+uCNxjpnb1yymVzx+iGbXv2h2/Zs4LNuvReKw7IpVQKGw6gFLapHzpnQFMNOmTcPJyYmgoCAKFSqEj48PM2fOpHnz5qxYsYL333+fX375JavyKnJIbm1XlR8lIeyb0a0s2i6vo+38GsobVxLXZDqQxrDsLStw2LICQyl3dI1aUS/+Ja7wbIFJSw9Y9l6LLA9rlqUrgDlw4ACjRo3Cy8uLBw8eAGB8OsV0z549OXDgAJMnT2bdunW2z6nIMbm1XdXef5SEEE8pFBjKeZNQzpuEnkO5vXMz5W+eT31Y9t3bOKxZxDIWMc2lPKvLNGZ/xSbMaO6bLG1urUW2ljysWZauAEar1VK6dGLvcCcnJwCio599uGrWrMmff/5pw+wJkTJ7/1ESQligVPLI04eElu0Th2WHHkF9YAfqI7tSHJZdKeoa70ddg7N/oL9YC23DQHR+z1bLzq21yNaShzXL0hXAeHp6cvPmTQAKFChA6dKlCQkJoWvXrkBiHxlnZ2fb59LOSfVf1rD3HyUhsprd//ao1ehr+aOv5U98wjhUx/ejObAd1YkDKa+WfeEkqgsnMS6dg756fXQNA/Gq28SuayzkYc2ydAUwTZs2ZcOGDXzwwQcA9OrVi/nz55tGI/3111+8/vrrWZJReybVf1kjtzZt2f1NQ+QZeeq3x8HxhWHZexJHMqU2LPvkQdQnDyaull2nMbpGaQ/Lzo3kYc2ydAUw77zzDk2bNiU+Ph5HR0c+/PBDoqKiWLNmDSqVit69e/Pxxx9nVV7tllT/5S956qYh7Fqe/e1xLoyuWXt0zdo/Wy37wPYUh2UrEuLRhAShCUl7WHZulFsf1nJaupuQPD09TX87OjoyZ84c5syZY/OM5SVS/Ze/5NmbhrA7+eG3x3y17MwPyxb2Q2bizQZS/Ze/5IebhrAP+e23J/mw7O2oD25H+Z+Vw7IbBqJt1Aqju1c251xkhAQw2UCq//KX/HbTyC7Styj98u1vj9mw7DdRXg5FvX8b6pCdKGOjLO6ivHsbh7WLcVi7GH05b3QNW6Fr2BJjCbfszXsuYQ/fNwlgRJ5n6YuYlfLtTSOLSd8ikSFKJQbfGiT41iCh/9uoQo+i3r8d9ZHdKOIeW9xFdf0yquuXcVz+A/pKNdE2bIWuQXMo4pK9ec9B9vB9kwBG5HmWvojzKudwpkS6Sd8ikWkqNfqafuhr+iUOyz6xH83+tIZln0J14RTGJd+aVsvW1W0CBQpmc+azlz183ySAEXmePXwRRdqkb5GwKQdH9A0C0DcIsG5YtsGQfFh2w5boa/nb3bBsa9jD900CGJHn2cMXUaRN+haJLJNsWHbqq2VbHJbdMBB91Tp2MSzbGvbwfZMARuR5lr6ICXeicjpb4jnWdBiUvkUiO9h6tWx7ZQ/fNwlgRLbKiZ7tlr6IF+9k6Smtkt3vRa4cVWA0gkHP6B0RnIt8goNBxxOjnsn/3mVx86KJ/RL0OtDrQadFodehr1gVHJ1yNt8iX8j0atkyLDtLSQAjspU99GzPLjZ7L4zGxBu8Xgc6Lej1phu/4707KAuoQa/j6+BINPfiKGfU4WDU8+ttBR/XLYhCrwOdzrS/IoVjJaVRJKV9uk3xXNrE17Vm+UnteAq9DoAgS+X613JxH81ahLFMufS/T0Jk1AurZSsvnUmcY8bqYdk+iZ1/8/Gw7KwgAYzIVrm2Q63RaOGm/OxGbbop63Sgf3pTNt2I9Sj02hfSp3KTf1qTMO5SLHEJWhwMOjRGPS4qPU5n1abXnwUVT/d/Poh48d8pqPbcvxdaSrDX1m9k1lPodRhzOhMi/1IqMVSqSUKlmukYln0J1fVLyYdli0yRACaTcmW1fE6xGASY37QbP/6PkjFP0Bj0OBh11NArUB278kKQoH16Y34+SLBwE7ciSEgpCKiREI8aA+j0zwKSbNbT0kbLNdPiebqUAzYhslUmh2VXrFANdWCXfDEsOytIAJNJNm8SsSIIsPbJPF3NAU9frxD9ACcnpzTOp31W6/C0BsLaIOA3Sxt3Z/ztyihl9p9SpMCoVIJaAyo1qNUYn/4fVeJ/RtO/NaBWYXRwzOksC5FcBoZlF718Gi6fxqhxeLZadh4dlp0VJIDJpP/i9XSJPEzne0dxMOgoptbjdFmdQk1B0o3fUhCQFIDkbJOKS46eXWSUUakkARUJSjU6pZpCThpUDhoS9EY0BQqCWmUeBKg0piDBLGBQazCqVKZgIjGA0Lzw97PgwvS3WgMq1dP9n6UdtjeGo1FGtAo1CQoVNVwL8Ff70olpk46hlHBS5DEWh2VvR3U51GJyhTYBzaGdaA7txFjQGV395nluWHZWkAAmk0o6qnjp4TUG3Ql+tvF2zuVHZJxWoUKrUJGgUGNUqSha0OG5m7zmaRCgMb+JWwoCzF5PvLGnGgSYah0sBwGJx0/cbh5UPAsC2vx7z1QTCODn6sCWTq5cvHgRX1/fdL0P1jaLWpMu5MgdrhR8FpQ7KlTgXDidV0YI+2U2LPvu7cTOv6kOy370bFh20eLo/JOGZVe162HZWUECmEz6qbkLe85KlXaS1JoDngUBKT3ZP7uBW7xpp1RTkHSuF4+XYlCh5krYDSpUqmQ6X73VkVyOfdY1tGIRFUdfKZ2D72T62LJztLXNotakk0kEhXjGWMrdbFh27Ma/cT1/LOVh2dH3cdjyDw5b/sHg6o6uUSDahoEYy5bP3oznUhLAZJJXYQ0+1YrCadsdM6kmQK9KbAp4/unf/Cb+wpO7pSDA4pN+yjUFtyP/o4yHp+WagLTOp1TZTXOA7l60WU1ACScNl2Of1WDY243WloGCtcGQNensYTZPIbLd02HZt1u+gvPQ960blh354mrZgej8W2IsaT8PWrYmAYwN6Gv6EVe46HM3+edqGkw1BUk1Cc81J7wQVPivvcf5WEzVhDlRCxB98SKl0tnkkBfY+43Wlvm3NhiyJp09zOYpRI6yNCz7wHbUh61ZLftH9L410DZqha5BQL5aLRskgLEJQzlvDOW8M32cogUc4aH91gLYM3u/0doy/9YGQ/Ye9AmR6zw/LHvg02HZB3agOr4/5WHZF0+junga45I56KvXR9eoVb4Zli0BTC4iNwSRG1gbDNl70CdErmZxWPZ2VKFHU14t+1QI6lMh+WZYtgQwuYjcEIQQQiTz4rDsQ8Go929L57DsluirvpSnhmVLACOEEELYCaNLCbSte6Bt3SNxWPbBHaj3bcuXw7IlgBFCCCHskLGUO9rOr6Ht1P/patnbUR/cjvK//DEsWwIYkevI+lJCCJEOz6+W3evpatn7t6VzWHYru1stWwIYkevYfH0pIYTILxQKDL41SPCtkY7VspOGZSetlh1oF8OyJYARuY4tZ5UVQoh8y9Jq2Qd2oDqxH4U2rdWynw7LbhiIrl4TKOCczZlPW66fNjU2NpaJEydSo0YNSpcuTZs2bTh69KjpdaPRyMyZM6lSpQqlS5emY8eOnD17NgdzLDLrxflvZD4cIYTIpKfDsuNGT+fRnFXEvTkBXfX6GBWWw4CkYdlOP83EeXR3HL+biurwbkiIz+aMpyzX18CMGTOGM2fOsGDBAsqWLctff/1Ft27dOHDgAO7u7nz77bfMmzePefPm4evry+eff0737t05dOgQhQvLonGpsdTXJDeQ+XCekf5AQgibK1gIXdP26Jra97BsRVRUlDHtZDnjyZMneHh4sGjRIjp27Gja3rx5c1q3bs2HH35IlSpVGDp0KO+9955pH19fXz7++GMGDRpks7xkZFXf3K7N+shkKxjPqxyV58ppib1cT0vXKD39geylnJkl5cxbpJw5wzQse/82VLeupZneULQ4Or8W6BqlPiw7q8qZq5uQdDoder0eJycns+0FChRg//79hIWFERERQcuWLc1ea9y4MQcPHszu7Nod6WuS+8k1EkJkl6Rh2U8+/Y3HM34hoWM/DCVTHpWkjL6Pw9Z/cFrwcTbm8plc3YRUuHBh/Pz8+OKLL6hatSpubm6sWLGCkJAQKlasSERE4lh3V1fzJ1JXV1fCw8NTPO7FixczlJ+M7pdbORsdAdVzfyc+6WeknDefKPi/Cw5EaRW4aIx8XCmBsgVybeUeYB/X09I1Sm++7aGctiDlzFuknLnASy2gTgDONy9T7EwILqGH0TyOTZbsbqWXCL90KdVDZaScadXa5OoABuCHH35g1KhRVKtWDZVKRe3atenZsyfHjx/P8DEzUpWV26r6bGFJaW2yviYJd65lqJyj1kdyKjYxALoRB59eL5qrhz7by/W0dI3S0wfGXsqZWVLOvEXKmctUqgQt2xOv16ELPYb6wDaz1bKLdOhFIY8KKe6eVeXM9QFMhQoV+Pfff3n06BGxsbGULl2aQYMGUb58edzcEqu2IiMj8fT0NO0TGRlJqVKlcirLdsPS2ksX72TsWNLUkTVkfSwhRK6hUqOv2QB9zQZPV8s+gOrCKQypBC9ZKVf3gXmes7MzpUuXJioqiu3bt9OhQwe8vLxwc3MjKCjIlC4uLo79+/fj7++fg7nNf2TosxBC5CMOjugbNCeh/9s5loVcXwOzfft2DAYDvr6+XL16lcmTJ1OpUiX69++PQqFgxIgRfPXVV/j6+uLj48MXX3yBs7MzPXv2zOms5ysy9FkIIUR2yvUBTExMDNOmTeP27dsUK1aMLl268NFHH6HRJPYDeOedd3jy5Anjx48nKiqKevXqsXLlSpkDJptJU4cQQojslOsDmO7du9O9e/cUX1coFEyaNIlJkyZlY65yP5kATQghRF5mN31gRKJrMVrarI+k7j93aLM+krBYy+tZJC2IeCVGT0hkAkODo7I3o0IIIUQWkgDGzlgbmMioICGEEHmZBDB2xtrAREYFCSGEyMskgLEz1gYmPzV3wc/VgYpFVPi5OsioICGEEHlKru/EK8xZO1xZRgUJIYTIyySAsTMSmAghhBCgiIqKyt0r7gkhhBBCvED6wAghhBDC7kgAI4QQQgi7IwGMEEIIIeyOBDBCCCGEsDsSwAghhBDC7kgA84IHDx4wfvx4GjRoQOnSpalevTrjxo3j/v37ae67Zs0a/P39KVWqFP7+/qxbty4bcpxxv/32G506daJcuXK4uLgQFhaW5j5Lly7FxcUl2X9xcXHZkOOMyUg5wf6uZ3x8POPHj6dixYq4u7vTp08fbt26leo+M2fOTHYtK1WqlE05tt7PP/9MrVq1cHNzo3nz5uzbty/V9Hv27KF58+a4ublRu3Ztfvnll2zKaeakp5y7d++2+F28cOFCNuY4/fbu3UufPn2oWrUqLi4uLF26NM19zpw5Q4cOHShdujRVq1bls88+w2jM3QNo01vOsLAwi9dz27Zt2ZTj9Pvqq69o0aIFnp6eeHt707t3b0JDQ9Pcz1bXUwKYF4SHhxMeHs60adPYt28fP/zwA/v27WPIkCGp7hcSEsLgwYPp1asXu3fvplevXrzxxhscPnw4m3Kefo8fP6Zly5ZMnDgxXfsVLFiQ8+fPm/3n5OSURbnMvIyU0x6v56RJk1i3bh0LFy7k33//JTY2lt69e6PXp74Olq+vr9m1TCs4yG4rV65k4sSJ/O9//2PXrl34+fnRq1cvbty4YTH9tWvXePXVV/Hz82PXrl2MGzeO999/nzVr1mRzztMnveVMcuDAAbPr5+3tnU05zphHjx5RrVo1Zs2aRYECBdJMHxMTQ/fu3SlVqhQ7duxg1qxZzJ07l++++y4bcptx6S1nkn/++cfsejZr1iwLc5k5e/bsYciQIWzevJm1a9eiVqvp1q0bDx48SHEfW15PmQfGClu2bKF3796EhYVRpEgRi2kGDRrEgwcPWL16tWlb165dKVmyJAsXLsymnGbMsWPHaNGiBSdOnMDLyyvVtEuXLuX9999P88k+N0pPOe3tekZHR+Pj48O8efN49dVXAbh58yY1a9ZkxYoVBAYGWtxv5syZrF27lv3792dndtMlMDCQ6tWrM2fOHNO2unXr0rVrV6ZMmZIs/ZQpU1i3bh1Hjx41bRs9ejTnzp1j69at2ZLnjEhvOXfv3k3nzp25fPkyJUqUyM6s2kzZsmX5/PPP6d+/f4ppFi5cyNSpU7lw4YIpEJg9eza//PILoaGhKBSK7MpuhllTzrCwMGrXrk1QUBAvvfRSNubOdh4+fEi5cuVYunQp7du3t5jGltdTamCsEBsbi6OjIwULFkwxzaFDh2jZsqXZtsDAQA4ePJjV2ct2T548oUaNGlSrVo3evXtz4sSJnM6Szdnb9Tx+/DhardYszx4eHlSuXDnNPF+7do0qVapQq1YtBg8ezLVr17I4t9ZLSEjg+PHjya5Fy5YtUyxXSEiIxWt37NgxtFptluU1MzJSziQBAQFUrlyZLl26sGvXrqzMZo4ICQmhUaNGZrUYgYGBhIeHW90cbE9ef/11fHx8aNu2ba6vNXzRw4cPMRgMuLi4pJjGltdTApg0REVF8cknnzBgwADU6pRXXoiIiMDV1XyKf1dXV+7evZvVWcxWvr6+fPfdd/zxxx/8/PPPODo60q5dOy5fvpzTWbMpe7ued+/eRaVSJXsSTyvP9evXZ/78+axYsYI5c+YQERFBmzZtrOrzlR3u3buHXq9P17W4e/euxfQ6nY579+5lWV4zIyPlLF26NF999RWLFy9m8eLF+Pr60rVr11zXBJhZKV3PpNfyikKFCvHxxx/z66+/8vfff9OsWTMGDRrEX3/9ldNZs9rEiROpWbMmfn5+Kaax5fXMN2shzZgxgy+++CLVNOvWraNp06amvx8+fEjfvn0pU6YM06dPz+os2kRGypkefn5+Zh9Of39/mjZtyg8//MDnn3+eoWNmRFaXM7ewtpwZ1bp1a7O/69evT506dfjjjz94++23M3xckfV8fX3x9fU1/e3n58f169eZM2cOjRs3zsGciYwoUaIEo0ePNv390ksvcf/+fb799lt69+6dgzmzzgcffMCBAwfYtGkTKpUqW86ZbwKYESNGmPoGpMTDw8P074cPH9KrVy8A/vrrrzQ7qbq5uREZGWm2LTIyklKlSmUwxxmT3nJmlkqlok6dOly5csVmx7RGVpfT3q7noUOH0Ov13Lt3j5IlS5pei4yMpFGjRlafr1ChQlSpUiXbr2dKSpQogUqlSte1KFWqlMX0arU61/YVyUg5LalXrx4rV660dfZyVErXM+m1vKxevXpWjdLKaZMmTWLlypWsW7eO8uXLp5rWltcz3wQwJUqUsPrHKzY2ll69emE0GlmxYgWFChVKc58GDRoQFBTEmDFjTNuCgoLw9/fPcJ4zIj3ltAWj0ciZM2eoUaNGtp0Tsr6c9nY969Spg0ajISgoyBR437p1i/Pnz6crz3FxcVy8eDHX1Fw5ODhQp04dgoKC6Natm2l7UFAQXbp0sbiPn58f69evN9uW1DFSo9FkZXYzLCPltOTUqVO4ubllQQ5zjp+fH1OnTiUuLs70IBkUFESZMmXS7Ixv7+zhek6YMIFVq1axbt06q6ZgsOX1lD4wL4iNjaVHjx5ERUUxf/58Hj9+TEREBBERESQkJJjSdenShWnTppn+Hj58OLt27eLrr7/mwoULfPXVV+zevZsRI0bkRDGsEhERwcmTJ7l06RIA58+f5+TJk2ZD4F4s56xZs9i+fTvXrl3j5MmTvP3225w5c4bBgwdne/6tlZFy2tv1LFq0KK+//jpTpkxh586dnDhxgrfeeovq1asTEBBgStegQQN+/PFH098fffQRe/bs4dq1axw+fJiBAwfy+PFj+vbtmwOlsGzUqFH88ccfLFq0iPPnzzNhwgTu3LnDoEGDAHjrrbd46623TOkHDRpEeHg4EydO5Pz58yxatMgumsTSW8758+ezfv16Ll++zNmzZ5k2bRobNmxg6NChOVUEqzx8+JCTJ09y8uRJDAYDN2/e5OTJk6bh4tOmTTML2nr27EmBAgUYOXIkoaGhrF27lm+++YaRI0fm6hFI6S3nH3/8wd9//8358+e5ePEic+fO5eeff2bYsGE5VYQ0vffee/zxxx/89NNPuLi4mO6VDx8+NKXJyuuZb2pgrHX8+HEOHToEJFbfPe/5PhVXr16lbNmyptf8/f355ZdfmDFjBp9++ikVKlTgl19+oX79+tmX+XT65Zdf+Oyzz0x/JzVVzJs3zzTc78VyRkdH884773D37l2KFClCrVq1+Pfff5O9V7lJRsppj9dz5syZqFQqBg0aRFxcHM2aNeP77783a4++ePGiWUfW27dv8+abb5qanurXr8/WrVspV65cThTBoh49enD//n1mz55NREQEVatWZfny5aY83rx50yx9+fLlWb58OR988AG//PILpUuX5rPPPqNr1645kX2rpbecWq2W//u//+P27ds4OTmZ0rdp0yYnsm+1Y8eO0blzZ9PfM2fOZObMmfTt25cFCxZw584drl69anq9aNGirFq1ivfee48WLVrg4uLCqFGjcn1Amt5yAnzxxRfcuHEDlUqFt7c33333Xa7u//Lzzz8DJPtuTZgwgUmTJgFk6fWUeWCEEEIIYXekCUkIIYQQdkcCGCGEEELYHQlghBBCCGF3JIARQgghhN2RAEYIIYQQdkcCGCGEEELYHQlghBAiBS4uLsycOdP09+7du3FxcWH37t05mCshBEgAI4QQQgg7JAGMEEIIIeyOBDBCCCGEsDsSwAghbOr69eu89957+Pn5UaZMGcqVK0fv3r05c+aMxbR9+vTB3d0dHx8fJk6cyPbt2y32Mzl69Ci9evWiXLlylC5dmnbt2rFr165kx7xz5w7vvvsu1apVo1SpUtSsWZMxY8YQGxtrShMdHc0HH3xAjRo1cHV1pXbt2nz++efo9XrbvyFCiCwhizkKIWzq2LFj7Nu3jy5duuDp6Ul4eDi//fYbHTt25MCBA5QuXRqAR48e0aVLF+7cucPw4cMpXbo0f//9t8UOsnv27OGVV16hZs2ajB8/Ho1Gw19//UWPHj1YtWqVaZHViIgIAgMDuXfvHgMHDqRq1aqEh4ezfv167t+/T+HChXny5AmdO3fm+vXrDB48mHLlynHkyBFmzZrFjRs3mDt3bra+X0KIjJEARghhU23atEm2Om2fPn3w9/dn8eLFjB8/HoBff/2Va9eu8fvvv5vSv/HGGzRr1sxsX6PRyNixY2nYsCGrV69GoVAAMHjwYJo1a8bHH3/Mli1bAJg6dSrh4eFs2bLFbOXwSZMmYTQmrls7f/58Ll68SHBwMJUqVTKd18vLixkzZjBmzBh8fX2z4J0RQtiSNCEJIWyqQIECpn8/fvzYVPPh4+PD8ePHTa9t374dNzc3unTpYtrm5OTEgAEDzI536tQpLl68SM+ePbl//z737t3j3r17xMbGEhAQwOHDh3n8+DEGg4ENGzbQunVrs+AlSVLgs2rVKho2bEiJEiVMx7p37x4BAQFAYm2PECL3kxoYIYRNxcXF8emnn7J8+XLu3Llj9lrx4sVN/75x4wbly5c3BRZJKlasaPb35cuXARg9ejSjR4+2eM779+/j4OBATEwMVatWTTV/ly9f5vTp03h7e1t8PTIyMtX9hRC5gwQwQgibev/991myZAnDhg3D39+fokWLolQqmTRpEgaDId3HS9pn6tSp1KlTx2KakiVLEhMTY/XxmjVrxrhx4yy+Xr58+XTnUQiR/SSAEULY1OrVq+nTpw+zZs0y2x4VFWVWA+Pp6UloaChGo9GsFubKlStm+1WoUAGAQoUKmZp5LHFwcKBIkSKcPXs21fxVqFCBhw8fpnosIUTuJ31ghBA2pVKpTB1mk6xYsYLw8HCzbS1btiQiIoK1a9eatsXFxbFo0SKzdHXq1KFixYrMmzfPbCh0kv/++w8ApVJJx44d2bp1K4cPH06WLilP3bt35+jRo6aOv8+LjY0lPj7eypIKIXKS1MAIIWyqffv2/PnnnxQuXJhq1apx6tQpVq5cmaxpZtCgQfz0008MHz6cY8eOmYZROzo6As863SqVSubOnUvPnj1p2LAh/fv3p2zZsoSHh7N3716MRiPr168HYMqUKezcuZNOnTrxxhtvUKVKFe7evcu6detYsmQJXl5ejBkzhk2bNtGvXz/69u1LnTp1ePLkCWfPnmXNmjXs3bsXLy+vbH3PhBDpJwGMEMKmZs2ahUajYdWqVSxZsoQ6derwzz//MHnyZLN0hQoVYu3atUyYMIEffvgBZ2dn+vTpQ4MGDRg4cCBOTk6mtC+//DJbt25l9uzZLFy4kNjYWEqVKkXdunXNRi2VLl2abdu28cknn/DPP/8QHR1N6dKladmyJSVKlAASR0mtX7+er7/+mlWrVvHXX39RqFAhvL29GT9+PG5ubtnzRgkhMkURFRVlTDuZEEJkj/nz5/PBBx8QGhqKu7t7TmdHCJFLSQAjhMgxT548MZs3Ji4ujmbNmqHX6zly5EgO5kwIkdtJE5IQIse8/vrreHh4ULNmTWJiYli+fDkXLlzgp59+yumsCSFyOQlghBA5pmXLlixevJi///4bvV5P5cqV+eWXX+jRo0dOZ00IkctJE5IQQggh7I7MAyOEEEIIuyMBjBBCCCHsjgQwQgghhLA7EsAIIYQQwu5IACOEEEIIuyMBjBBCCCHsjgQwQgghhLA7EsAIIYQQwu5IACOEEEIIuyMBjBBCCCHsjgQwQgghhLA7EsAIIYQQwu5IACOEEEIIuyMBjBBCCCHsjgQwQmSRiIgIRowYQY0aNShevDguLi6EhYXZ/Dy7d+/GxcWFmTNn2vzYKVm6dGmWnzMsLAwXFxdGjBiRZecQzyR9juT9FvZCAph8ysXFBRcXl5zORrbK7h/okSNHsmzZMmrWrMn//vc/JkyYQNGiRa3ef8OGDabrtHLlyizMqbCFmTNnmq6Xi4sLxYoVw8PDgxo1atCjRw9mzZrF1atXcyx/uSlAWbNmDePHj6d9+/Z4enri4uLC4MGDU0z/4ntr6b86depkOD979uwxPWRMnTo12esxMTFMnDiR9u3bU6VKFdzc3PDx8aFly5bMnz+fR48eWdxn3LhxVK1alQoVKtC7d+8Ur/+2bdsoXrw4Bw8ezHAZ8iN1TmdAiLwoISGBoKAgfH19WbZsWYaO8dtvv5n9u0ePHjbKnchKL7/8Mk2aNAHgyZMn3L17l8OHD7Njxw5mz57NiBEjmDZtGiqVKodzmnNmz57N6dOnKVSoEO7u7sTGxqaaPun9tGTTpk2cOHGC1q1bZygv0dHRDB8+nIIFC/Lw4UOLaR48eMDvv/9O3bp1adOmDSVLliQmJoZdu3bxwQcfsGjRIjZv3mz2gDJy5EjWr19P9+7dKVmyJEuXLqVLly4cPHiQggULmtLFxMTw7rvv8tZbb+Hv75+hMuRXEsAIkQUiIiIwGAyUKlUqQ/tfv36d7du306hRIyDx6fnKlStUrFjRltkUWaBJkyZMmjQp2fagoCDefvttvvvuO548ecKXX36ZA7nLHT799FPKli1LxYoV2bNnD507d041fdOmTWnatGmy7Xq9niVLlgAwcODADOVl/PjxPHr0iHHjxjF9+nSLaTw8PLh+/ToajSbZa8OGDWP58uX8/PPP/O9//wPg7t27rF+/ntdff525c+cCUK9ePYYPH87mzZvp3r27af/Jkyej0WiYPHlyhvKfn0kTkjBJ6nPQsWNH7t69y6hRo/D19cXd3Z02bdqwb98+AB49esTkyZOpUaMGpUqVwt/fn9WrVyc73vP9JA4cOECXLl3w9PTE09OTnj17cvz48WT7hIeH89lnn9G2bVsqVaqEq6srVapUYciQIZw9ezbFvB89epTBgwdTtWpVXF1dqVSpEp07d+aPP/4AEqugk34kly1bZlb1vHTpUqven5MnT/LGG2/g6+uLq6sr1atX5+233+batWtm6WrWrEnNmjUB2Lt3r+k86am6X7RoEQaDgX79+tGvXz+MRiO///671fsniYqKYsaMGTRu3Bh3d3c8PT1p1KgRH330EVFRUWZpr127xsiRI6lWrRqurq74+vryxhtvcPr06VTPcfLkSV599VXKlStHmTJl6NChQ4pV4bGxscyYMYMGDRrg5uZGuXLl6Ny5Mxs2bEh32SwxGo0sWrSIVq1a4eHhQZkyZWjatClz585Fq9UmS1+zZk1cXFzQ6XR8+eWX1K1bl1KlSlG9enWmTJlCQkKCTfIF0KJFC/755x8cHBz45ZdfOHHiRLI0V65cYfTo0abvlre3N/3798/0dyW9n/+wsDAGDx5MxYoVcXNzIyAggE2bNtnmjQCaNWuGt7c3CoUiU8fZsmULt27dokGDBtSoUSPd+69evZrly5cze/Zs3NzcUkynUqksBi8AXbt2BeDy5cumbTdu3ADgpZdeMm1L+nfSawDBwcEsXryYOXPmmNXKCOtIDYxIJjo6mrZt21KsWDF69erF7du3WbNmDa+88gpbt27lnXfe4eHDh3To0IHY2Fj++ecfBg0aRNmyZWnQoEGy4x05coSvv/6aFi1aMHToUC5fvsy6devYu3cvq1evNqs23bdvH9988w1NmzalS5cuODs7c/nyZdauXcvGjRvZuHEjtWvXNjv+okWLGDt2LEqlknbt2uHr68u9e/c4ceIECxYsoF+/fjRp0oTr16+zbNkyatSoQceOHU37JwUbqdm6dSuvvfYaer2ezp07U6FCBc6cOcOSJUtYv349a9eupVatWgCMGDGC69ev8/333+Pp6Um/fv2sPg+ATqdj6dKlFCxYkG7duqFQKJg4cSJ//PEHH330UYo/pC+6du0anTt35saNG9SqVYs33ngDSPyh/fnnn3n11VdN/aCOHz9O165diYmJoU2bNlSvXp2rV6+ybt06Nm3axB9//EHLli2TneP48ePMmTOHBg0aMGDAAG7evMnatWvp2rUru3fvxtfX15Q2Ojqa9u3bExoaSq1atRg+fDjR0dGsXr2a/v37M2nSJCZMmGBV2VIyfPhw/vrrL9zd3enXrx8ajYZNmzYxefJkgoKCWL58OWp18p+9N998k/3799OqVSsKFy7M1q1b+fbbb4mMjGT+/PmZytPzqlSpQrdu3Vi+fDn//POP2Wc5ODiY/v37ExcXR9u2bfH29iY8PJx169axbds2/vjjDwIDA03p0/NdSc/n/8aNGwQGBlK+fHl69+7NgwcPWLVqFf369WP16tU0a9bMLH3NmjW5ceMG69ats1hLkpWSmlkzUvsSHh7O2LFj6dq1Kz179rT6QeZFSYHd8wGUh4cHkBjcJ0kKWD09PYHEB8HRo0czaNCgbH/f8goJYEQyp0+fZtiwYXz22WemJ6Qvv/ySjz/+mE6dOtG0aVMWLlyIg4MDAC1btmTo0KF88803Fn8Etm3bxuzZsxk6dKhp25o1axg4cCBvv/02ISEhpvM0a9aMCxcuULhwYbNjnDp1inbt2jF9+nT++ecf0/Zz584xbtw4nJ2d2bhxI9WrVzfb7+bNmwCmH4ikTrWWqvhT8ujRI4YPH45Wq032A75o0SLGjBnD8OHD2bt3LwqFgpEjRxIWFsb3339PuXLl0nUuSPxBDA8Pp0+fPqb3oUuXLixbtowNGzbQrVs3q44zbNgwbty4wQcffMD7779v9lpUVJTpRm40Gk3BxPz5800BF8DOnTvp3r07w4YN4+TJk8meEjdv3sy8efPo37+/aduvv/7K2LFj+f77782aSaZNm0ZoaCj9+/fnu+++M13z8ePH07JlS2bNmkXr1q2pW7eu9W/Wc1auXMlff/1F9erV2bhxI0WKFAFgypQp9OzZkx07drBgwQJGjx6dbN+rV69y4MABihUrBiRW6zdp0oQ///yTKVOmpPp0nl5NmjRh+fLlHD582LQtOjqaQYMGodFo2LZtG1WqVDG9dv78eQIDAxk1ahQnTpzA0dERSN93JT2f/z179jBx4kQmTpxo2tarVy9eeeUV5s6dmyyAySm3bt1i27ZtFClSJN39w4xGIyNHjkSj0fDVV19ZvZ9Op2P27NlAYr+Y/fv3c+rUKZo1a2bWCdnNzY0OHTrw+++/8/DhQ4oVK8ayZcvw8PCgTZs2AEydOhWDwWCx07CwjjQhiWScnZ35v//7P7Pq3d69ewPPmiSSgheAHj16oNFoOHXqlMXjVaxYkSFDhpht69q1K35+fly8eNGsucHV1TXZDzIkPuU1bdqUPXv2mDUFLFy4EJ1Ox3vvvZcseIFnT0KZsWHDBu7du0eXLl2S/XgPGDCA2rVrExoayqFDhzJ9Lnj2VPl8IJEUIDzfsTc1x48fJyQkhGrVqvHee+8le93FxYVChQoBcPDgQc6dO0fdunXNzgkQEBBAp06d+O+///j333+THadhw4ZmwQvAa6+9hlqt5siRI6ZtWq2WP//8k4IFCzJ9+nSzz1bZsmUZN26cqfkno5L2nTJliil4AXBwcODTTz8FSLEZbtq0aabgBRK/A7169cJgMHDs2LEM58kSd3d3AO7du2fa9ueff3L//n0mTJhgFrwAVK5cmQEDBnDnzh2Cg4NN29P7XbGWp6cn48ePN9sWGBiIh4eH2TVNsnbtWkJCQqhXr166z5UZixcvRq/X07t373Q3v/zwww8EBQXx9ddfU6JECav30+l0fPbZZ3z22Wf8+OOPnDp1ij59+rB06VKcnJzM0i5YsICBAweyZ88e/v77bxo3bsyaNWtwdnZm3759LFy4kDlz5uDs7MyMGTPw9fWlZMmStG3bNsXfUmFOamBEMhUrVjTd3JKULl0agKJFi+Ll5WX2mkqlwtXVldu3b1s8XqNGjVAqk8fKL7/8MiEhIZw8eZKGDRuatm/evJlffvmF48ePc+/ePXQ6ndl+9+7dM+Un6Sm2VatW6Syl9ZKqflN68gwICODEiROcOHECPz+/TJ3r+vXr7Nixg3LlyplVK7/88suUL1+e4OBgrl27Rvny5VM9TlIw1bJlS4vv/fOsKd+6des4ceIEPXv2NHvN0tBVjUZDqVKlzPrYXLhwgcePH1O/fn2LN4yAgACzvGRE0r6WquNr1KiBq6srly5d4uHDh8k+35bKkRT8vthXKLOMRiOAWRCXFMSfOXPG4tw6ly5dAhJrY5Ke4CF93xVr1axZ0+IIKQ8PD0JCQpJtr1ChQrqObwsGg8HUeTepadRa58+fZ+rUqfTp08esKc0aTk5OREVFYTQauXPnDjt37mT69Om0aNGCFStWmH0vixYtyjfffJPsGE+ePGH06NH069ePli1bMnfuXL788ks++OAD6taty5QpU+jVqxdHjx6VfjFpkABGJPP802uSpOYGS69BYhDz4o9nkpRG4ri6ugKJwwiTLFiwgEmTJuHi4kKLFi3w8PCgQIECKBQKNmzYwOnTp4mPjzelj46OBp491WaFpPylVI6k5oWkvGRGUufdvn37mt3gFAoF/fr149NPP+X3339nypQpqR4nKS9lypRJ85yZKV9K89qoVCr0er1NzmGtmJgYihQpQoECBVI8R2RkJDExMckCGEtzIiXdxJ8vhy2Eh4cDULJkSdO2+/fvA4m1Cql5fr6R9H5XrJXaNTUYDOk+XlbYunUrN2/epEGDBhZrXlMzbNgwSpQowWeffZbh8ysUCsqUKUPfvn3x9fWlVatW/O9//zNr3k7JjBkzePz4MZ988gkA8+bNIyAgwFTr5ezsTLt27VixYgUDBgzIcB7zAwlgRJa7e/euxe2RkZHAs6BIp9Mxa9Ys3NzcCA4OTvbkaKmJJunH9vbt21k2MV9S/lIqR0REhFm6jNLpdKanyqRqakv++OMPPvjgg1Q78ya9L0k3y9RkR/my6xwPHjzgyZMnFoMYW12nzNq9ezcA9evXN21LytPOnTutmpAtI9+VvCSpKTW9tS/wrKbuxZrkJN988w3ffPMNHTp0MI1iTE39+vUpWrQoe/fuxWg0pjqy6tChQyxYsIClS5dStGhRoqOjuXPnDq+++qopTdJggHPnzqWnWPmSBDAiyx04cACDwZCsKWPv3r3Asy/svXv3iI6OplmzZsl+kB8+fGixeaF+/focO3aMbdu2Ua1atVTzkdEn6qSRHLt377Y4W+iuXbsAy80Q6bFx40bu3LlDhQoVUpy46+DBg1y4cIF///3XNHzTkqTRYDt27LD43j/v+fJZktTvIjPlq1SpEgULFiQ0NJR79+4la0ayxTlq167Nzp072bNnT7JJzUJDQ4mMjMTHxydZ7Ut2OnfuHGvWrEGhUJg1xzVo0IC1a9eyf/9+q96DjHxXsqpGKbuFh4ezZcuWDHXeBXj99dctbr98+TL79u2jevXq1K1b1/S7lJbY2FhiY2NNtV8piY+P5+2336Znz560b98eeNaM+HxNmS2H7ud10olXZLnLly+zcOFCs21r1qwhJCQEX19f0zBqV1dXChYsyPHjx81mxNRqtUycONGs02OSIUOGoFar+eKLLwgNDU32+q1bt0z/Ll68OPBsZJK1OnbsSPHixVmzZo0p6EqydOlSjh07RtWqVS0OIU+PpKfKCRMmMHfuXIv/ffjhh0DKnVGT1KlTB39/f0JDQ/niiy+SvR4dHW16j/39/alcuTJHjhzhr7/+MksXHBzMunXrKFGiBB06dMhw2TQaDb179+bx48dMmzbN1A8EEm9IX3/9NQqFgtdeey3D50i6MU2fPj3Z5yfpfcvJKvng4GB69uxJQkICQ4cONRt2+9prr+Hi4sLs2bMt9jMxGo3s37/fdHPLyHclo5//tFy9etXUxyk7PN95N6XmQoDHjx9z4cKFZNP3p/TdSuqM3rp1a+bOnWs2avLMmTPExcUlO0dCQgLjx4/HYDCY9U2yZNasWTx48IBZs2aZthUpUgR3d3eCgoJMTfBbtmwBSNaZWyQnNTAiywUGBvLhhx+ybds2qlevbpoHpkCBAsydO9f0FKJUKnnrrbf4+uuvady4MR06dECr1bJ7924ePHhA06ZNk9USVKlShS+//JKxY8cSEBBgmgfmwYMHnDx5kvj4eNM+vr6+eHh4sH//foYOHYq3tzcqlYr27dunOgmWs7Mz8+fPZ8CAAXTr1o0uXbpQvnx5Tp8+zZYtWyhatCgLFizI1KRcYWFhBAUF4eLikuow6Q4dOuDq6kpQUBBhYWEpVoND4kiLTp068emnn7JhwwZT59arV6+yY8cONm/eTK1atVAoFCxYsIBu3boxfPhwVq1aZZoHZu3atTg4OPD9999nukPhlClT2L9/P4sWLeLkyZMEBASY5oF58OAB77//vlmzSnq98sorbNq0ib///puGDRvSsWNH0zwwly5donnz5owcOTJTZbDGnj17TB1x4+PjiYiI4NChQ1y6dAmVSsWYMWOSDZ0tVqwYixYt4rXXXqNNmzY0a9aMKlWqoNFouHXrFocPH+bmzZtcu3YNBweHDH1XMvr5T0uXLl3SPQ/M+vXrTZMXJjUrHj582GyyxwULFiTbz2AwmPoJpdV8dOTIETp37oynp2emR/UsXryYP/74A39/fzw9PSlatCjh4eEEBQURERGBt7e3qU+LJcePH2fu3LksXLjQFEgmGTVqFB9++CGdO3emRo0aLFu2DHd3d3r16pWpPOcHEsCILFe/fn3Gjx/PJ598wo8//ggkzko6efLkZNXlH374ISVKlGDx4sX89ttvFClShICAAD766KMUVz4eOHAg1apVY+7cuRw4cICNGzdSvHhxKleuzJtvvmlKp1KpWLJkCVOnTmXz5s3ExsZiNBpxd3dP8we8Xbt2bNmyha+++org4GDWrFmDq6srffv25f33309zVFBakjrvvvrqq8mGYz5Po9HQt29f5syZw6JFi1Kdfrx8+fLs2rWLuXPnsn79en766SccHR3x8PBg6NChlCtXzpS2bt267Ny5k9mzZ7Nz5062b99O0aJF6dixI//73/+srk5PjYuLC5s3b+bbb79l7dq1zJ8/H0dHR2rVqsVbb71Fly5dMn2OH374gcaNG7N48WIWL16MwWDA29ub6dOnM3z4cIuT2Nna3r17TXMCFSxYEBcXFypVqkSvXr3o3bt3ip+VZs2asXfvXr777ju2b99OSEgIarUaNzc3/Pz8mDp1qln/nfR+VzLz+be1U6dOJVsj7Pr161y/ft30t6UAZvv27dy4cSNDnXczo1u3bjx69IhDhw4REhLCw4cPKVy4MFWqVGHUqFEMGTIEZ2dni/tqtVpGjhxJhw4dLDb7jhgxgujoaBYtWsTRo0epV68es2fPTrV2SSRSREVFGdNOJkT6LV26lFGjRjFhwoR0T+YmhBBCpEb6wAghhBDC7kgAI4QQQgi7IwGMEEIIIeyO9IERQgghhN2RGhghhBBC2B0JYIQQQghhdySAEUIIIYTdkQDGShcvXszpLGQLKWfeIuXMW6SceYuUM3NkJl6RpmsxWobtiuK/eD0lHVX81NwFr8Ipr4QshBBCZDWpgRFpGrYripDIBK7E6AmJTGBocFROZ0mIFF2L0dJmfSR1/7lDm/WRhMVqczpLQogsIAGMSNN/8fpU/xYiN5GAW4j8QQIYkaaSjqpU/xYiN5GAW4j8QfrAiDT91NyFocHmfWCEyK1KOqq4gt7sb5H7PXr0CJ1OB4CTkxPR0dE5nKOsJ+UEZ2fnDK8SLwFMJuWHDq5ehTVs6eSa09kQwioScNuf+Ph4AIoWLQqAo6MjTk5OOZmlbJHfy2k0GomKiqJw4cIZCmIkgMmkpPZ2gCvoGRocJTd7IXKQBNz2Jy4ujiJFiuR0NkQ2UygUuLi4EBMTYwpe00MCmEyypr09P9TSCCFEZigUipzOgsgBmbnu0ok3k6zp4CqjIoQQQgjbkgAmk35q7kIvx7tcPPQ/1l39gRWKXSivnoenndFARkUIIYQQtparm5B++uknfv31V27cuAFAlSpVeO+992jbti0AI0aMYNmyZWb71K9fn23btmVbHr0Ka/jV4w5Oj+5Q4dEdCNsFgFHjgKF8JfTe1egV5cFSynPTsTgoFDIqQgghRLr07t2b4sWLs2DBAgA6duxItWrVmD17doaPOWLECO7fv89ff/1lq2xmq1wdwLi7uzNt2jS8vb0xGAwsW7aM/v37s3PnTmrUqAFAQEAAP/zwg2kfBweHbM+n6nJosm0KbQKqi6dRXTzNJ8AnQIRTMc6W8KVK0Vooz9XAUKEyOBbI9vwKIWxH+riJnLBkyRKrR+7s3r2bzp07c/nyZUqUKGHaPmvWLIxGY1ZlMcvl6gCmY8eOZn9PnjyZhQsXcujQIVMA4+joiJubW05kz0R59ZxV6dziHuB2KwRuhcAaMCqVGDwqYvCuit67GnrvahhLe4JSWvaEsBcyElFYKyEhwWYP2cWKFcv0MTIy8ic3sZs7pV6v559//uHRo0f4+fmZtu/fvx8fHx/q1avHmDFjiIyMzPa8PfngWx5P+ob4V99CV68pBpcSae8EKAwGVNcvoQlah9PPn+E8aSDOozrj9Pl7OKz8BdXx/RAblbWZF0JkivRxy786duzI2LFjmTBhAl5eXnh5eTF58mQMBgMANWvWZObMmYwaNYpy5coxdOhQAA4ePEiHDh2oUKECVatWZdy4ccTExJiO+/jxY0aMGEHZsmXx9fXlyy+/tHju8ePHm/5OSEhg+vTp1KhRg1KlSlG7dm2+//57wsLC6Ny5MwDe3t64uLgwYsQIILEJqXfv3qZjxMfHM3HiRHx9fXFzc6NVq1bs37/f9Pru3btxcXEhODiYwMBAypQpQ0BAAMePHzeliY6OZtiwYfj4+ODm5kbt2rX58ccfbfBuJ5era2AAzpw5Q5s2bYiLi8PZ2ZklS5ZQvXp1AFq1akXnzp3x8vLi+vXrzJgxgy5durBz504cHR1TPGZGl/ZOdT+VM1Sqn/if0Ygm5gHOt65Q8PbVxP+Hh6HUpb2onOLxI9RnDsOZw6ZtccVK8bhsBR6VrcjjshV44uaJUZV1l06WeM9b8ms5lQlxGByyfpIwZ6MjoHru74Qsfc/z4vV0cnJK9ptdaGBAtubhvx82pXsfg8HA8uXL6d27N+vXryc0NJT33nuPEiVKMHz4cIxGI/PmzePdd99l8+bNGI1Gjh07Rvfu3Rk/fjxffPEFUVFRTJ48mREjRrBw4UIAJk2aRFBQED///DOlS5fmyy+/ZO/evXTo0IG4uDjTuXU6nenv4cOHc/DgQT7++GNq1qzJjRs3uH37NiVLlmThwoUMGTKE4OBgihUrhpOTE3Fxcej1evR6vekYH330EWvXruWrr77Cy8uLH374gVdeeYX9+/fj5uZGQkJiTeOUKVP46KOPcHNzY/LkyQwdOpRdu3ahUCiYNm0aZ86cYdGiRbi6unL9+nXu3btnOoclMTEx3L17N9l2X1/fVN//XB/A+Pr6snv3bmJiYlizZg0jRoxg/fr1VKtWjVdeecWUrnr16tSpU4eaNWuyefNmunTpkuox0+vixYvp36++v+mfj3U6lDevoLwciuryWVSXzqCMuGnVYZwe3MXpwV2Knz4IgFGjweCV2EE4qfnJWMINbDCPQobKaYeknHmLpXIW+L9h4OCANqATugYB4Jg1wcyS0tpkM/9mVR+YvHo9o6OjzWZqTe1ml1UyMiOuUqk0BRgKhYKaNWty/fp1fvzxR959910UCgUvv/wy7733nmmft956ix49ejB27Fji4uJwcnLi66+/plmzZsTGxlKgQAGWLVvGd999R/v27QH4/vvvqVatGiqVypRPpVKJWq3GycmJy5cvs3r1alasWEGrVq0AqFy5sumcpUqVAsDDw8OsD4xKpTId89GjR/z+++/MmTPHVGMzZ84c9u7dy+LFi/noo49MzV+TJ08mMDAQgIkTJ9KuXTvu379P2bJluX37NnXq1KFx48ZA4v02qZwpKVKkCJ6enul+/3N9AOPg4EDFihUBqFOnDkePHmX+/Pl89913ydKWKVMGd3d3rly5kt3ZTJtajaF8JQzlK6EL7Ja47WEMqitnUV0ONQU2iscP0zyUQqtFdekMqktnTNsMRYtheNqPxuBdDX35ylCgYBYVRojcTXn1PKqwCwCoLp7GuGQuukat0AZ0wuBl2wBAZv7N3+rXr282GZufnx+ffPKJqUnopZdeMkt/4sQJrly5wqpVqzAajSgUClNH2qtXr1KgQAESEhLMukoUKlTI1PJgycmTJ1EqlTRt2jTD5bh69SparZaGDRuatqlUKvz8/Dh3zryf5/N5KV26NACRkZGULVuWIUOGMHDgQI4fP06LFi1o164d9evXz3C+UpPrA5gXGQwGUzXWi+7du0d4eHiOd+q1WqEi6Gv5o6/1tKbGYEARcRPV02BGeTkU5Y3LKJ62p6ZGGf0A5dG9qI/uBcCoUGIoW/5pUFMVg3c1DO5e0kFY5Aua4PVmfyuePEKzYw2aHWvQl6+UWCvTMBAKOOdQDvMmGZGVnLOz+WfMYDAwYMAARo4cSXx8vFnTWZkyZbh06VJ2ZzFNL86Wq9Fokr2WFIS1bt2aU6dOsXXrVoKDg+nduzedOnUyGy1sK7k6gJk6dSpt2rShbNmyPHz4kBUrVrBnzx6WL1/Ow4cPmTVrFl26dMHNzY3r168zffp0XF1d6dSpU05nPWOUSoxlyqErUw5dk3aJ2+LjUF678DSoCUV55SzK+2l3VFYYDahuXkF184rpx9zoVBB9xSrP1dRUxVgk8z3Z8xP5gbYDRiOKqPspvqy6dgHVb19h/GM+Ov8WibUy3tVs0gSb39lyRNbD33faMGdZ58iRI6aaFIBDhw5RpkyZFNd2ql27NmfPnqVixYoWm1YqVKiARqPh0KFDlC9fHkhcqTs0NNT094tq1qyJwWBg9+7dpiak5yU1/ej1KXcwr1ChAg4ODhw4cIAKFSqY0oeEhNCzZ89U34MXlShRgj59+tCnTx9at27NkCFDmDNnTqp9UzMiVwcwERERDBs2jLt371KkSBGqV6/OihUrCAwM5MmTJ4SGhvLnn38SHR2Nm5sbTZs25ddff6Vw4cI5nfUMsXxzdMJQuRaGyrVI6gKsuH8X5eWzic1Pl0JRXjuPIiE+zeMr4h6jDj0KoUdN2wyuZcz60ih0qRwg3XnPezd2GTJrBxQK4t79BMWdm2iCN6DeswllzIPkyRLi0OzeiGb3RvRly6ML6IS2cRsoJIsKZlR+HJF1584dJk6cyJtvvkloaChz5swxGx30onfeeYfWrVszduxY+vXrR4kSJbhw4QKbNm3im2++oVChQrz++utMnTqVkiVLUrp0aT7//HPTyCZLfHx86N69O2PGjGHmzJnUrl2b27dvc/36dfr06YOnpycKhYLNmzfTvn17nJycKFSokNkxnJ2dGTx4MFOnTqVEiRJ4eXkxf/58IiMjefPNN61+Pz755BNq165N1apV0el0rFu3Di8vL5sHL5DLA5ikGQctKVCgACtXrszG3GQ9a2+OxuKl0Bcvhb5B88QNOh3KW1ef9qN52vwUft2qcyojw1FGhsOB7QDUUqkxlvdNDGoqVkPvUw1jydJpPp3mlxt7fvyBtlfG0h4k9H6LhFcGozq2D03welSnD6OwMHGX6tY1VEu/w2H5D+jqN0fXvCP6KnWkViadSjqquILe7O+8rlevXhgMBgIDA1EoFLz++uuMHDkyxfQ1atTg33//ZcaMGXTv3h2DwUD58uXN5j37+OOPefToEa+99hoFChRg2LBhPH78ONV8fP/993zyySdMnDiRe/fu4e7ubsqHu7s7kyZNYsaMGYwZM4Y+ffpYvL9OmzYNgFGjRhEdHU2tWrVYsWKFqZ+LNRwdHZkxYwZhYWE4OjrSoEEDFi1aZPX+6aGIioqy32n4slF29P6v+88drsQ8+/JXLKLi6CvWf3DMPIpFdeWcWVCjeBST9n4WGAq7mPWl0VeskqzvgE3zng0yej3brI80BWoAfq4OuTpQy6ujVl5kbTkVkeFodm9CvfvfNJtiDW4eaJt3RNekLcaixW2V1UzJ7dczLDZjI7Kio6PNJlVLa9RKbpHZ6fztpZyZlVY5X7z+1srVNTD5jU2fXpwLo6/ZAH3NBolNT0Yjiohbz0Y8XTmL8volFKm0iSZRxkahPL4P9fF9ABgVCgzuXs/60lSsSikHZ54f+5VXn7x+au6S7Ada2A+jaxkSegwiodsAVCdDEmtlju+32FFeGXETx+U/4PDPz+hfehlt847oa9QHZd78bNuCjMgS2UkCmFwkS2+OCgXG0h7oSnvAy20StyXEowy7aBrxpLocivJeRNqHMhoTq9xvXUOz618Agh0LcLxIRQ4X9eFKqcoMfqme7fKei8gPdB6hVKGv0wh9nUYoHvyHes8mNMEbEptTX6DQ61Ef3oX68C4MJd3QNu2Arll7jMVL5UDGhRBJpAnJSrm96tZWrh49hI8x7llQc/UciviMTSplKOFm1kHY4OULDrbvyJUWSx2ME+5cyxfXM798bm1SToMB1dmjqHduQH1kNwp9yj3ajQol+lp+aAM6oa/dELJwZuzn5dXraa9NSJkl5UwkTUjCJnSFXdD7+qKv93RCJIMe5c1rz5qdLoWiDA+z2BHyRcp7EYk1OiFBABhVKgzlfNBXrGpqfjK6lc1wR0lrRz5Z6mA8r3KyZCK/UyrRV6+Pvnp94mOi0OzdjCZ4PcrwG8mSKowG1CcOoD5xAINLCXRN26Nt1gFjKfccyLgQ+ZMEMCJ1ShWGct4Yynmja5E4vTSPH6K6ei5xKPfTpidFbHSah1Lo9aiunkd19TxsXw2AsVCRxJW4Kz7XQdjZumHw1o58kpFDIt2KuKBt3xttu1dRXjiFZud61Id2otAmn0RTGXUPh3VLcFi3BF31euiad0JX92XQ2GbVYSGEZRLAiPQrWMj0pGrqIBwZ/rSD8NOlEcIuploFn0TxMMb0JJvEUKZcYlCTNIOwRwWLVfTWBibZPbQzv8yJky8oFBgq1yK+ci3iXxuNZt9W1MEbUN24bDG5+swR1GeOYCxcFO3LbdE274jR3SubM22fnp8MTuQfRitq81MiAYzIPIUCYyl3dKXcodHTWSAT4lFev2RqdlJdDkX53x2rDqcMv44y/DqaPYmrwxodnDBUqGwW1BiLlbQ6MLHUOTrhTlSmipya/DInTr7jXBht6x5oW3VHefV8Yq3Mwe0o4p4kS6qIjcZh03IcNi1HX6nW0wUlm+dIHzB74OTkxOPHj5NNuy/yNqPRSFRUVIYnn5UARmQNB0cMPtUx+FSHp4OeFNH3TTMIJ/apOYciLvXJmSBxxlTV+ROozp8wbTMUd2WjVxUW6cuzr7A3d0r58F0Ko7YsjRy6aF0slSHSZJV3pFSbZqhYhfiKVYjvOxL1wR1ogjegunLW4jFUF06iunAS45I5aBu3Rte8E4Zy3tlcktzN0dERnU5HdHRiU3RMTEyKU/HnJVJOKFy4MGp1xkIRCWBEtjEWLY6+7svo676cuMGgR3k77Fmz0+WzKG9dta6D8P1Iit6PZDS7Gc3TDsJHvdH7JM5Lo/ephtHNI0dmUs2Ps5HmVWnWphUoiC6gE7qATiivX0YdvB7Nvq0WV5VXPH6Iw7ZVOGxbhb5i1cRJ8hq2BCdZNR7MFz28e/cunp6eOZib7CHlzBwJYETOUaoweFTE4FERXfOn02g/eYzq2vnEZqcriZPuKaOTr2PzIoVejyrsAqqwC886CDsXRl+x6rOh3BWrZss6N9bO5yN9ZXK/9NSmGcp5k/D6OyT0Ho76UDCanetRXThpMa3qSmJNpHHZPHT+LdE274ShYhVZukCIdJAAJo+y5uZoKU2OK1AQfdWX0Fd96VkH4f/uPG12SuogfAGFVpvWkVA8ikV9KgT1qRDTNkNpT/QVq1KySEmUagMGT2/IYPVlSqyd7E76yuR+GapNc3BE93IbdC+3QXE7DE3wBjR7N1scqaeIe5L4evAG9J7e6Jp3RNu4tdUj8YTIzySAyaOsuTnaxfwoCgVG1zLoXMuAf8vEbTotyuuXny2LcDkU5d3bVh1OeecGyjs38ATY9AdGjQOG8pUSOwj7JC5gaSzumi1PwtJXJudYW/uV2dmxje5eJPQdSULPNxMXlNy5DvWZIxbTqm5cRrVkDg5/fY+uQQDagE4YKtWUWhkhUiABTB5lzc3Rbm+g6sROlIaKVaB1j8RtMVGorjxdiftpR2HFk0dpHkqhTUB18TSqi6dN2wwuJZ/OHvy0+alCZXAsYPNiSF+ZnGNt7ZfNlo7QOKD3C0DvF5C4oGTwBtS7N6KMupcsqUKbgGbfFjT7thDv5gktOqF9uS0Uccl8PoTIQySAyaOsuTnmqRtoERf0dRqjr9M48W+DAUX4dVRJzU5XQlHeuIrCmHzRvhcpo/5DeWQ36iO7gcRp4w2eFZ5OtJdYU2Ms7QlKZaayLAtD5pycCN6f1fooKOnUlZ+mvUbFK0cSF5Q8cdDiZ9Mx4gb8uQCHv39CV68phX3qgLd3pj97QuQFEsDYGVtWfWf3/CjZSqnEWLY8urLl0TVrn7gt7jHKaxdQXQ7lyYlDFIm4bvEJ+EUKowHV9cuorl9GE7QOAGNBZ/QVqprWedJ7V4XCLunKoiwMmXNyInhPVuuzJ5YtnRJH5Snu30W9exORG9fi8eS/ZPsq9Do0IUH4hARh2Pon2mYd0DVtj7FYySzPtxC5lQQwdsaWVd/ZPT9KjnMqiKFKHQxV6nC1UgN8fXxQ3I809aNRXQ5Fee2CxeniX6R4/Aj1mcNw5rBpm6GU+9MRT4nrPBnKeYNaRhXlRjlR+5VarY+xeCm0XQfQW9mWIheOMiQ8iC7/HcHBmLxmSBkZjuM/C3FY9Sv62o3QNu+IvpZfti0oKURuIZ94O2O3/VZyI4UCY4lS6EuUQu8XkLhNp0N54zLKK2dRXXq6gOWd5Iv5WaK8ezuxM/H+bQAYNRoMXpUS13nySVzvyViytHTKzAVyovbLmlqfHwOKM1RRnw/Kv8SvxocsVB2k1IFNFj+DCoMB9bG9qI/txVCs5LMFJV3LZGk5hMgtsiSACQ0N5YcffuD48ePExMRgMJi37SoUCo4fP54Vp87z7L3fSq6f+0StxlChMoYKldEFdkvc9jAmcd6OpFFPV86heBSb5qEUWi2qS2dQXToDWxK3GYoWM/WlMfhUQ1++MhSQiczyA2tqfZIHVj487tYP5fkTifPKhOxEaWGNMeWD/3BYuxjNuiWJa5QFdET/0st2XQOY638rRI6zeQCzf/9+unfvTpEiRXjppZc4efIkzZo1Iz4+npCQEKpUqUKdOnVsfdp8w947fubWuU9S/bEsVAR9LX/0tfwT/zYYUETcfNZB+PJZlDcuoTBY0UE4+gHKo3tRH90LPO0gXLa8qS+NwbsaBncv6aSZB2W41kehwFClDvFV6nClcScqR1xBvXM9qptXkic1GlGfPoT69CEMhV3QNW2XuKBk6czPgprdAUVu/a0QuYfNA5hPPvkET09Ptm3bhk6nw8fHh3HjxtG8eXMOHjzIq6++yowZM2x92nzD3jt+5tYmsHT9WCqVGMuUQ1emHLombRO3xcehDLuQ2Oz0tKZG+SB5Z8wXKYwGVDevoLp5BU3wBgCMTgXRV6zyrC+Nd1WMRYrZpJzCvukLOD9bUPLK2acLSu5AER+XLK0yNgqHf//E4d8/0VepjbZ5J3T1m2V4QcnsDihy62+FyD1sHsAcP36c999/n6JFi/LgQeIU8ElNSP7+/gwcOJBPPvmEli1b2vrUwg7k1iawTP9YOjphqFQLQ6VaJM0RrLgfmdiX5vLT+WmunkOREJ/moRRxj1GHHoXQo6ZtBtcyz5ZE8K6GoZwPaBzSl0eRZ1yL1THsrCv/uQygXPveLHI6htvBjaiunreYXnXuBKpzJ54uKNkGXUBHDB4V03VOWwYU1tTm5NbfCpF72DyAUSgUFC1aFICCBRPb9u/fv2963cfHh59//tnWpxV2Irc2gWXFj6WxuCv64q7o6zdL3KDXobx51XzUU7iVHYQjw1FGhsOB7YnHVmswePkk9qV5Oozb6FpGOgjnE+a1IWp6ur7MlqndUIZdRB28Ac3+rSgeJ5/IUfEoFoet/+Cw9R/03tUSF5T0b2HVgpK2/I5YU5uTW38rRO5h8wCmXLlyXLt2DUhcIt3Ly4ugoCBeeeUVAPbt20fx4sVtfVphJ3JrE1i2/Fiq1Bi8fDF4+aJr2TVx26NYVFfOPRfUnEXxKCbNQyl02qd9cM7C1n8AeOBYhGMuPlx0rUTbgJcoWb267csgcoWUakMMXr4kDHj36YKSOxM7/j43y/TzkoJo4x/z0DUMRBvQEUP5yikGwbb8jlhTm5NbfytE7mHzAKZly5asWrWKKVOmADBw4ECmT5/O9evXMRqN7Nmzh3fffdfWpxUiU3Lsx9K5MPqaDdDXbPBs8cqIW8+NeDqL8volFPq0q+uLxcfQMuIoLSOOwuk/MSoUVClRGnW1lxKbnSpWxeBRHpRSFW/v0qwNcXRC16QduibtUNy69mxByYfJg2NF3GM0O9eh2bkOfTkftAGd0DVqBQULmaWz5XdEmoeELSiioqKMtjxgVFQU165do3r16mg0GoxGI19++SVr1qxBpVLRvn17xo0bh0ZjX8PhLl68iK+vb05nI8tJOXOhhHiUYRdNI55Ul0NR3ovI0KGMTgXQl69sanYyeFfD6FLCxhnOfnZ1PTMhqZxhsdpktSFpjgjSJqA+sht18IbEPlapMDo4ovMLQNu8EwbfGjZvmkwr//nteuZ1WVVOmwcweZV80PIWey+nIure02ansygvh5Jw8SwF9Wl3ELbEUNLNrC+Nwcs3wyNVcoq9X09r2aqciohbaHb9m7igZPT9VNMa3L3QNu+E9uXW6V4uI6PkeuYtWVVOmzchffbZZ/Ts2RNvb29bH1oI8ZTRpQT6ek3R12sKQFh0HLPWncbr7nn8oy/ROu4KjhHXURjTfj5R/heB8r8ICAlKPLZKjaGct6nZSe9TDWOpstJBOA8xupUloddQEroPQnViP5rgDahOhlhcUFJ5OwzHZfNw+PtHdPWaomveEX3Vl+xqriKZFC9vsnkNTFIH3Ro1atCzZ0+6d++Op2fmJ1HKaRIp5y35opyPH3Jn93Y842OeTbgXG5WhQxkLFXm6aOXToKZiFXAubLOsZvYGky+uJ1lbTsW9u6h3/Ytm178o799NNa2hlDvaZh3RNW2XJU2Qti5nm/WRplFPAH6uDrmig7B8bjPH5jUwp06dYuXKlaxevZopU6YwdepU6tevzyuvvEK3bt1wc3Oz9SlFHiNPS89k6r0oWIjYitXQ+vo+6yAcGW5qdlJdCUV57SIKC1PTv0jxMAb1iQOoTxwwbTO4e6F/WkOT2EG4QoYXFJRZV3OesUQptN3fQNv1dVSnDqMJXo/q+D6LHciVd2/juOInHFYuRF+nMdqATuhrNsi1HcRlUry8yeYBTNmyZRk9ejSjR48mLCyMf/75h1WrVjFx4kQ+/PBDGjVqRM+ePRk4cKCtTy3yCLmZPWPT90KhwFjKHV0pd2gUmLgtIR7l9UuJo52SFq+MDLfqcMrbYShvh6HZswkAo4MThgqVn9bUPO0gXKykVceSG0wuolShr+2PvrY/iqh7qPdsQhO8IXGh0hcoDAbUR/egProHQ/FS6Jo9XVCyRO56UJVRT3lTlq5G7eXlxbhx4xg3bhyXLl1ixYoVzJ8/n3379kkAI1IkN7Nnsvy9cHDE4FMdg091aJO4SRF9P3G005WzzxavjHuc5qEUCXGozp9Adf6EaZuhuOvTxSuf1tR4VQJHp2T7WnODkZq5rJHa+2p0KYG2U3+0HfqiOn8C9c71qA/vQqHTJjuO8v5dHFb/jmbNIvQ1/dA274S+TiNQZ+ltxioyKV7elC2frKNHj5qalWJjYylc2Lq2859++olff/2VGzcSZyutUqUK7733Hm3bJq4/YzQamTVrFr///jtRUVHUq1ePL774gqpVq2ZZWUTWk6elZ2z5XlgbAFxVFGbY7Ur8p/CmZPWu/DS8MBVibz8bwn35LMpbV63rIHw/EuX9YNSHggEwKpUYPH1MzU56n2oY3TysusFIzVzWsOp9VSrRV30JfdWXiH8YjWbvFtQ7N6C6fS3Z8RRGI+qTB1GfPIihaDF0TZ4uKOnmkQ2lsUwmxcubsiyAOXXqFKtWrWLlypVcv34dJycnWrduzSeffGIKQNLi7u7OtGnT8Pb2xmAwsGzZMvr378/OnTupUaMG3377LfPmzWPevHn4+vry+eef0717dw4dOmR1kCRyH3laesaW74W1AUCydLtj2dKpIgaPiuiad0xM9OQxkWfOsCnoGL7/XeClBxcpER+dZh4UBgOqsAuowi7A9tUAGJ0LU7liVXZ6V0Xvm1hbQ6HkgZXUzGWNdL+vhYqibdsLbZueKC+dSVxQMiTI4jpfyugHOGxYhsOGZeiqvoSueSd09ZrY3TB9kTvZPID59NNPWbVqFZcvX0atVtOiRQs++OADOnToQKFChdI+wHM6duxo9vfkyZNZuHAhhw4donr16ixYsIB3332Xrl0Tp2VfsGABvr6+rFixgkGDBtmsTCJ7ydPSM7Z8L6y9UVmVrkBB+t8pT0hJdyjZEYxGujhH80f5iGd9aa5dsNjU8CLFo1jUp0LgVIhpm6G0p1lfGoNHRamZy4AsXTRRocDgW4N43xrE938b9YHtaHZuSAxOLVCfPYb67DGMzkXQvtwGXfOOiR2/hcggmwcwX331FU2aNGH06NF06dIFFxcXmxxXr9ezevVqHj16hJ+fH2FhYURERJital2gQAEaN27MwYMHJYAR4gXW3qisTWcW2CgUnFaXQOdfHfyffie1CSivX05sdnq6KreljqCWKO/cQHnnBpq9mwEwahzYWq4Sa1QVWOnmx+3SVfJ1zZy1sm3RxIKF0LXsiq5lV5TXLiTWyuzfZrHvlOJRDA5bVuCwZQV6n+pom3dC5x8AjgUyUEKRn9l8HpjIyEhcXW339HzmzBnatGlDXFwczs7O/PTTT7Rt25aDBw/Stm1bTp06ZTbPzKhRowgPD2flypUpHvPixYs2y58Q9uLWEwX/d8GBB1oFLhojH1dKoGyB5F9/a9MNPuHIqdhnwU3Nwnp+qZ36bMDqR7EUvH0V51tXKHjrKs63r6KKf5Kuctxs04dIv8B07ZNf9TjsxI24ZxPOeToZWFk/LlvOrUyIxyX0MCWO76bQzcupptU7FuB+dT/uvdSUJ2W8siV/ltx8+tmPSuOzL7JHWnPHZMlSArt372bJkiWEhYURFRWF8YXOfgqFggMHDqSwt7mEhARu3rxJTEwMa9as4ffff2f9+vXExsZmOIDJCJlwKG+RcmaeNevxpNmEYTCguHMD1aXQpzU1oShvXLU4I2ySx/+3AIO3eUd9uZ6W5ZYJ3JQ3r6AO3oBm79Y0V1vXe1XiVrUGFO/aDwo4Z1MOE2X3+yWf28yxeRPS3LlzmTJlCk5OTvj4+FCypHXzQKTEwcGBihUrAlCnTh2OHj3K/Pnzee+994DEGp/nA5jIyEhKlSqVqXMKIdJmTf+cNJswlEqM7l7o3L3QNWufuC3uMcprF1A9t9aTMuoeAEaNBoOXT5aUJy/KLR3iDR4VSeg/moRew1Af2YM6eD3qs8csplWFXaBc2AWM2/9B598CbUAnDN7VUlzKwpbD6+29o3h+m2ogSwIYf39//vzzT4oWLWrrw2MwGEhISMDLyws3NzeCgoKoW7cuAHFxcezfv5/p06fb/LxCiPTL0A3BqSCGKnUwVKnzbAbh+5GJgcyDSFDn3R9kW8t1HeIdHNE1CkTXKBDFnZtodm1AvXsTypgHyZIqEuLQ7N6IZvdG9GXLowvohLZxGyhUxCydLYfX23tH8fw21YDNA5i4uDheffVVmwQvU6dOpU2bNpQtW5aHDx+yYsUK9uzZw/Lly1EoFIwYMYKvvvoKX19ffHx8+OKLL3B2dqZnz542KIkQIrNsckNQKDCWKIW+RCns63lYpMZY2oOEV98ioccQVMf3odm5HtXpQxbnF1LduoZq6Xc4LP8BXb1m6AI6oa9SBxQKm9aa5JYaq4yy9xqk9LJ5ANO0aVNOnz5tk2NFREQwbNgw7t69S5EiRahevTorVqwgMDCxA98777zDkydPGD9+vGkiu5UrV8ocMELkEvZ+QxDZQK1GX78Z+vrNUPx3B82ujbBjLQ6xFmpltFo0B7ajObAdg1tZtM074mtswBUKmtJkptYk19VYPWVt05C91yCll8078d6+fZsePXrQu3dvXnvtNZuOSMpJ0tkqb5Fy5i1Szrzl4vnzVHly/+mCkvtRGFLu1G1UqthZpj5LPFtw3qMOPwSUsJt+H9ZeT2s7F1vTsT4n2E0nXnd3d/r378/UqVP5+OOP0Wg0KJVKszQKhYLbt62bD0IIIUQ+o1Sir9MIfZ1GKB7892xBSQsLjSoMelrcOkiLWwcxlHBDq++Arll7jMXzzmAOa5uGcmsNUlaxeQDz8ccf8/XXX+Pu7k6dOnUoUqRI2jsJIYQQFhiLlUTb+TW0HfuhOnsUdfAG1Id3o9DrkqVV3ovAcdWvOKz+HX0tP7QBndDXbgiqnF9QMjPyW9OQtWx+VX/77Tfatm3L0qVLk9W8CCGEEBmiVKKvXh999frEx0ah2bsFzc71KMOvJ0uqMBpQnziA+sQBDC4lni0oWcrdJlnJ7uHK0pfMMpsHMFqtljZt2kjwIoQQImsUdkHb7lW0bXuhvHgKzc4NiQtKahOSJVVG3cNh/VIc1i9FV60uuoBO6Oo2AY1Dhk+f3cOV81vTkLVsHsC0a9eOvXv3ylpEQgghspZCgaFSLeIr1SK+/9to9m9DHbwe1XXLSxeoQ4+iDj2KsXBRtC+3TayVcU//0gX5bbhybmXzAOa9995jyJAhvPPOO7z++ut4eHigUiVvr8sro5OEEELkAs6F0bbqjjawG8qr5xMXlDy4HUVc8rW2FLHROGxajsOm5egr1UxcULJBc3B0supU0icld7B5AOPv7w/A6dOnWbx4cYrp7t+/b+tTCyGEyO8UCgwVqxBfsQrx/UaiPrADTfAGVFfOWkyuunAK1YVTGJfOQduoNbqAThjKpb5chS37pOS36f9tyeYBzPvvv48ihTUrhBBCiGzjVDCxz0tAJ5TXL6PetQHN3i0oHj9MllTx+BEO21fjsH01+gpV0AZ0QuffEgoUTJbWln1S8tv0/7Zk8wBm0qRJtj6kEEIIkSmGct4kvDaGhFffQn0oOLFW5vwJi2lVV8+hunoO4x/foWsYiLZ5JwwVq6S4oGRmSH+ajLPvwfFCCCFEejg4onu5DbqX26AIv44meAPqPZtRxkYlS6qIj0MTvAFN8Ab0HhWfLijZGpxtt1yN9KfJOBnrLIQQIl8ylilHQp8RPP7mb568PRVdjQYYU6hlUd28guOSOTi/8wqOP3yC8twJsLDwZHr91NwFP1cHKhZR4efqIHO8pIPUwAghhMjf1Br0DQLQNwhAERmOZte/qHdtRBn1X7KkCm0Cmn1b0ezbiqGMJ9pmHdE2aQdFXDJ0apnjJeMkgBFCCCGeMrqWIeGVISR0G4jqZAianetRnTiAwph8QUll+A0c//oehxU/o6vbBF1AJ/TV6oJSKaOLsoEEMEIIIcSLVGr0LzVG/1JjFPcjUe/eiGbXvyj/u5MsqUKvQ3NoJ5pDOzGULI22WQc+SPAj5GFiX5ncNLooLwVWEsAIIYQQqTAWd0XbdQDazq+hCj2Keud61Ef3WF5Q8r87OK78hVX8yr8lXmJhmRZsLF4714wusuWw7ZwOhiSAEUIIIayhVKKvUR99jfokxDxAvWczmuANKO/cSJZUhZHO947S+d5RbjkUY4t3SxSRPTG6lsmBjD9jy2HbOT2HjQQwQgghRDoZixRD26EP2va9UZ4/iSZ4PepDwRYXlCyb8IBBZ//BOH4l+ur10QZ0RP/SyzmQa9sO287pOWwkgBFCCCEySqHAUKU28VVqE//aGDT7tqLeuR7VzSvJkxqNqE8fQn36EIbCLrhX90NR+HWMpT2zLbu2XAYhp+ewkQBGCCGEsAXnwmhb90DbqjvKK2efLii5A0V8XLKkytgo3A5sgQNb0FeujbZ5x8QFJR0cszSLthy2bctgKCMkgBFCCCFsSaHA4F2NeO9qxPd7G/WB7WiC16O6et5ictX5E6jOn8C4dC7axq3RNe+EwbNiNmfanDUddHN6DhsJYIQQQoisUqAguhad0bXojDLsIurgDWj2b0Xx+FGypIpHsThsXYnD1pXovauibd4JnX8LcEq+oGRWy+kOutaQAEYIIYTIBgYvXxIGvEtC7+GoDwWj2/Q3hW5csphWdfksqstnny4o2QptQEcM5StnyYKSluR0B11rSAAjhBBCZCdHJ3RN2nLRrSKVnB0SF4zcswnFw5hkSRVxT9DsXIdm5zr05XzQBnRC16gVFCyUpVnM6Q661pDFHIUQQogcYnT3IqHvSB59s4K4kf+HrlrdFNOqrl/CadE3iQtK/jQT5YWTNllQ0hJ7WGRSamCEEEKInKZxQOffEp1/SxQRtxIXlNy9EWX0/WRJFQnxaPZsRrNnMwZ3L7TNO6J9uQ0UdrFZdnK6g641JIARQgghchGjW1kSeg0lofsgVCf2ownegOpkiOUFJW+H4bhsPg5//4SuXhN0zTuhr/oSKPN+A4sEMEIIIURupFajr9cUfb2mKO7dfbag5L2IZEkVOi2ag0FoDgZhcHVPnFemaTuMLiVyIOPZQwIYIYQQIpczliiFtttAtF1eQ3X6cGKtzLG9KPTJRwcpI2/juOInHFYuRF+nMdqATuhrNgBl7uuImxkSwAghhBD2QqlCX8sffS1/FNH3ny4ouR5lxK1kSRUGA+qje1Af3YOheCl0zdqjbdYBYwm3HMi47UkAI4QQQtghY9HiaDv2RduhD6pzx1EHb0B9OBiFVpssrfL+XRxW/45mzSL0Nf3QNu+Ivk5jUNtvGGC/ORdCCCEEKBToq76EvupLxD8cg2bflsQFJW9dS570/9u777Aori4OwL+lK20V6UUQUVFRYgFjAiJEYwMrghp7r4nGAkaDJiqoScwnijEqSVCwN8BeULAFG6JiQZSiAhKQLn2/P3BHll1gga1w3ufxkZ25M3Puzu7OmXvvzHA4UIr9F0qx/6JCuxXKvhyE0n5DwdE3kXzcjUQJDCGEENJUaGihdOAYlA4YDYWEOChfPQWlW5fBKhHwQMmc91A5tR8qp/ajzPozlPUbirKeDmJ/oKSoyPR1Vr/99hv69+8PU1NTWFpawsPDA3FxcTxl5s6dCzabzfPvq6++klLEhBBCiAxgsVDRvguKpy9HwdajKJqyBOXmHWosrvTkPtT+WAf179yhEuwPhdevJBhsw8h0C8y1a9cwffp09OjRAxwOBxs2bMCIESPw77//olWrVkw5Jycn7Ny5k3mtoqIijXAJIYQQ2dNCHWX93VDW3w0KSfFQvhIOpZsXwfog6IGSuVA5fxQq54+ivH2Xjw+UdAJUW0g+7jrIdAJz7Ngxntc7d+6EmZkZbt26hcGDBzPTVVVVoa/fNEZVE0IIIeJS0dYKxZMXo9hzDpSir0D5yikovngksKzii8dQfPH44wMlXVDabygqLDpKOOKayXQCU11+fj4qKirAZrN5pt+8eRPt27eHtrY2vvjiC6xevRq6urJ9C2RCCCFEalRboMxhMMocBkPh9SsoXT0F5evnwSoQ8EDJDwVQjgiFckQoyttaVbbKfO4i9gdK1kWuEhgvLy/Y2NjAzs6OmfbVV1/B1dUVbdu2RXJyMtatWwc3NzdcuXIFqqryMRCJEEIIkZYKEwuUTFiAEveZULp7DUpXw6H05L7AsopJ8VAM2gLOgR0os3NCqdMwVLTvArBYEo4aYGVnZ4vnUZYitnLlShw7dgxnz56Fubl5jeVSU1NhY2ODwMBAuLm5CSwTHx8vpigJIYQQ+aeS9Q5tYqLQ+sENKAtolanqg54Jnk3/ARxF0baJWFlZ1TpfLlpgvL29cezYMYSFhdWavACAoaEhjIyM8PLlyxrL1PWmCBIfH9+g5eQN1bNpoXo2LVTPpkW262kF2H+B4rIylMXcgPKVcCg+ug0Wh7/NQ9nIFO07Wde4JnHVU+YTmBUrVuD48eMICwtDhw41XwLGlZmZidTUVBrUSwghhDSWkhLKezmivJcjWP+lQTnyDJSiTkMhK4MpUtpvmHRCk8pWhbR06VIcPHgQ+/btA5vNRnp65RM41dXVoaGhgfz8fPj5+cHNzQ36+vpITk7GTz/9BF1dXQwbJp03lBBCCGmKOG0MUDJqKkpGTIJibHTlM5iSXqC8u71U4pHpBGb37t0AgOHDh/NMX7FiBby9vaGoqIi4uDgcOHAAOTk50NfXh4ODA/766y9oampKI2RCCCGkaVNQRLnt5yi3/RwoKQZEPPZFWDKdwGRnZ9c6v0WLFnz3iiGEEEKIhEjxsQNycxUSIYQQQgiXTD8LiRBCCCFEEEpgCCGEECJ3KIEhhBBCiNyhBIYQQgghcocSGEIIIYTIHUpgqnn//j2WLVuG3r17w8DAAF26dMGSJUuQlZVV57InT56Evb099PT0YG9vj7CwMAlE3HB///03hg0bBjMzM7DZbCQlJdW5THBwMNhsNt+/oqIiCUTcMA2pJyB/+7O4uBjLli1Du3btYGRkBE9PT7x586bWZXx9ffn2pTB3vJa03bt3o1u3btDX10e/fv1w48aNWstfu3YN/fr1g76+Prp3747AwEAJRdo49alnVFSUwO/i8+fPJRhx/V2/fh2enp6wtrYGm81GcHBwncs8fvwYQ4YMgYGBAaytrbFx40ZwBNzSXpbUt55JSUkC9+fFixclFHH9/fbbb+jfvz9MTU1haWkJDw8PxMXF1bmcqPYnJTDVpKamIjU1FWvXrsWNGzewc+dO3LhxA9OnT691uejoaEybNg3u7u6IioqCu7s7pkyZgjt37kgo8vorLCyEs7MzvLy86rVcy5Yt8ezZM55/ampqYoqy8RpST3ncn97e3ggLC8OePXtw+vRp5OXlwcPDA+Xl5bUuZ2VlxbMv60oOJO3YsWPw8vLC999/j8jISNjZ2cHd3R0pKSkCyycmJmLs2LGws7NDZGQklixZguXLl+PkyZMSjrx+6ltPrlu3bvHsP0tLSwlF3DAFBQXo3Lkz/Pz80KJFizrL5+bmYuTIkdDT08Ply5fh5+cHf39/bNu2TQLRNlx968l19OhRnv3p6Ogoxigb59q1a5g+fTrOnTuH0NBQKCkpYcSIEXj//n2Ny4hyf9J9YIRw/vx5eHh4ICkpCVpaWgLLTJ06Fe/fv8eJEyeYacOHD0ebNm2wZ88eCUXaMPfv30f//v3x4MEDtG3bttaywcHBWL58eZ1n9rKoPvWUt/2Zk5OD9u3bY/v27Rg7diwA4PXr17CxscGRI0fg4uIicDlfX1+Ehobi5s2bkgy3XlxcXNClSxds3bqVmdajRw8MHz4cPj4+fOV9fHwQFhaGe/fuMdMWLlyIp0+f4sKFCxKJuSHqW8+oqCi4uroiISEBOjo6kgxVZIyNjbFp0yZMmDChxjJ79uzBmjVr8Pz5cyYR2Lx5MwIDAxEXFwcWiyWpcBtMmHomJSWhe/fuiIiIwGeffSbB6EQnPz8fZmZmCA4OxuDBgwWWEeX+pBYYIeTl5UFVVRUtW7assczt27fh7OzMM83FxQX//vuvuMOTuA8fPqBr167o3LkzPDw88ODBA2mHJHLytj9jYmJQWlrKE7OJiQk6duxYZ8yJiYno1KkTunXrhmnTpiExMVHM0QqvpKQEMTExfPvC2dm5xnpFR0cL3Hf3799HaWmp2GJtjIbUk8vJyQkdO3aEm5sbIiMjxRmmVERHR+Pzzz/nacVwcXFBamqq0N3B8mTixIlo3749vv76a5lvNawuPz8fFRUVYLPZNZYR5f6kBKYO2dnZWL9+PSZNmgQlpZqfvJCeng5dXV2eabq6unj37p24Q5QoKysrbNu2DSEhIdi9ezdUVVUxaNAgJCQkSDs0kZK3/fnu3TsoKirynYnXFXOvXr0QEBCAI0eOYOvWrUhPT8fAgQOFGvMlCZmZmSgvL6/Xvnj37p3A8mVlZcjMzBRbrI3RkHoaGBjgt99+w969e7F3715YWVlh+PDhMtcF2Fg17U/uvKZCQ0MDP//8M/766y8cPnwYjo6OmDp1Kg4ePCjt0ITm5eUFGxsb2NnZ1VhGlPtTpp+FJErr1q3DL7/8UmuZsLAwODg4MK/z8/Mxbtw4GBoa4qeffhJ3iCLRkHrWh52dHc+H097eHg4ODti5cyc2bdrUoHU2hLjrKSuErWdDDRgwgOd1r169YGtri5CQECxYsKDB6yXiZ2VlBSsrK+a1nZ0dkpOTsXXrVvTt21eKkZGG0NHRwcKFC5nXn332GbKysvC///0PHh4eUoxMOCtXrsStW7dw9uxZKCoqSmSbzSaBmTt3LjM2oCYmJibM3/n5+XB3dwcAHDx4sM5Bqvr6+sjIyOCZlpGRAT09vQZG3DD1rWdjKSoqwtbWFi9fvhTZOoUh7nrK2/68ffs2ysvLkZmZiTZt2jDzMjIy8Pnnnwu9PQ0NDXTq1Eni+7MmOjo6UFRUrNe+0NPTE1heSUlJZseKNKSegvTs2bPJPeC2pv3JndeU9ezZU6irtKTN29sbx44dQ1hYGMzNzWstK8r92WwSGB0dHaF/vPLy8uDu7g4Oh4MjR45AQ0OjzmV69+6NiIgILFq0iJkWEREBe3v7BsfcEPWppyhwOBw8fvwYXbt2ldg2AfHXU972p62tLZSVlREREcEk3m/evMGzZ8/qFXNRURHi4+NlpuVKRUUFtra2iIiIwIgRI5jpERERcHNzE7iMnZ0dwsPDeaZxB0YqKyuLM9wGa0g9BXn48CH09fXFEKH02NnZYc2aNSgqKmJOJCMiImBoaFjnYHx5Jw/7c8WKFTh+/DjCwsKEugWDKPcnjYGpJi8vD6NGjUJ2djYCAgJQWFiI9PR0pKeno6SkhCnn5uaGtWvXMq/nzJmDyMhIbNmyBc+fP8dvv/2GqKgozJ07VxrVEEp6ejpiY2Px4sULAMCzZ88QGxvLcwlc9Xr6+fnh0qVLSExMRGxsLBYsWIDHjx9j2rRpEo9fWA2pp7ztT21tbUycOBE+Pj64cuUKHjx4gNmzZ6NLly5wcnJiyvXu3Rt//vkn83rVqlW4du0aEhMTcefOHUyePBmFhYUYN26cFGoh2Pz58xESEoKgoCA8e/YMK1asQFpaGqZOnQoAmD17NmbPns2Unzp1KlJTU+Hl5YVnz54hKChILrrE6lvPgIAAhIeHIyEhAU+ePMHatWtx6tQpzJw5U1pVEEp+fj5iY2MRGxuLiooKvH79GrGxsczl4mvXruVJ2saMGYMWLVpg3rx5iIuLQ2hoKH7//XfMmzdPpq9Aqm89Q0JCcPjwYTx79gzx8fHw9/fH7t27MWvWLGlVoU5Lly5FSEgIdu3aBTabzRwr8/PzmTLi3J/NpgVGWDExMbh9+zaAyua7qqqOqXj16hWMjY2Zefb29ggMDMS6deuwYcMGWFhYIDAwEL169ZJc8PUUGBiIjRs3Mq+5XRXbt29nLverXs+cnBx8++23ePfuHbS0tNCtWzecPn2a772SJQ2ppzzuT19fXygqKmLq1KkoKiqCo6Mj/vjjD57+6Pj4eJ6BrG/fvsWMGTOYrqdevXrhwoULMDMzk0YVBBo1ahSysrKwefNmpKenw9raGocOHWJifP36NU95c3NzHDp0CCtXrkRgYCAMDAywceNGDB8+XBrhC62+9SwtLcWPP/6It2/fQk1NjSk/cOBAaYQvtPv378PV1ZV57evrC19fX4wbNw47duxAWloaXr16xczX1tbG8ePHsXTpUvTv3x9sNhvz58+X+YS0vvUEgF9++QUpKSlQVFSEpaUltm3bJtPjX3bv3g0AfN+tFStWwNvbGwDEuj/pPjCEEEIIkTvUhUQIIYQQuUMJDCGEEELkDiUwhBBCCJE7lMAQQgghRO5QAkMIIYQQuUMJDCGEEELkDiUwhBBSAzabDV9fX+Z1VFQU2Gw2oqKipBgVIQSgBIYQQgghcogSGEIIIYTIHUpgCCGEECJ3KIEhhIhUcnIyli5dCjs7OxgaGsLMzAweHh54/PixwLKenp4wMjJC+/bt4eXlhUuXLgkcZ3Lv3j24u7vDzMwMBgYGGDRoECIjI/nWmZaWhu+++w6dO3eGnp4ebGxssGjRIuTl5TFlcnJysHLlSnTt2hW6urro3r07Nm3ahPLyctG/IYQQsaCHORJCROr+/fu4ceMG3NzcYGpqitTUVPz9998YOnQobt26BQMDAwBAQUEB3NzckJaWhjlz5sDAwACHDx8WOED22rVrGD16NGxsbLBs2TIoKyvj4MGDGDVqFI4fP848ZDU9PR0uLi7IzMzE5MmTYW1tjdTUVISHhyMrKwuampr48OEDXF1dkZycjGnTpsHMzAx3796Fn58fUlJS4O/vL9H3ixDSMJTAEEJEauDAgXxPp/X09IS9vT327t2LZcuWAQD++usvJCYm4p9//mHKT5kyBY6OjjzLcjgcLF68GH369MGJEyfAYrEAANOmTYOjoyN+/vlnnD9/HgCwZs0apKam4vz58zxPDvf29gaHU/nc2oCAAMTHx+Pq1avo0KEDs922bdti3bp1WLRoEaysrMTwzhBCRIm6kAghItWiRQvm78LCQqblo3379oiJiWHmXbp0Cfr6+nBzc2OmqampYdKkSTzre/jwIeLj4zFmzBhkZWUhMzMTmZmZyMvLg5OTE+7cuYPCwkJUVFTg1KlTGDBgAE/ywsVNfI4fP44+ffpAR0eHWVdmZiacnJwAVLb2EEJkH7XAEEJEqqioCBs2bMChQ4eQlpbGM69169bM3ykpKTA3N2cSC6527drxvE5ISAAALFy4EAsXLhS4zaysLKioqCA3NxfW1ta1xpeQkIBHjx7B0tJS4PyMjIxalyeEyAZKYAghIrV8+XLs27cPs2bNgr29PbS1taGgoABvb29UVFTUe33cZdasWQNbW1uBZdq0aYPc3Fyh1+fo6IglS5YInG9ubl7vGAkhkkcJDCFEpE6cOAFPT0/4+fnxTM/OzuZpgTE1NUVcXBw4HA5PK8zLly95lrOwsAAAaGhoMN08gqioqEBLSwtPnjypNT4LCwvk5+fXui5CiOyjMTCEEJFSVFRkBsxyHTlyBKmpqTzTnJ2dkZ6ejtDQUGZaUVERgoKCeMrZ2tqiXbt22L59O8+l0Fz//fcfAEBBQQFDhw7FhQsXcOfOHb5y3JhGjhyJe/fuMQN/q8rLy0NxcbGQNSWESBO1wBBCRGrw4ME4cOAANDU10blzZzx8+BDHjh3j65qZOnUqdu3ahTlz5uD+/fvMZdSqqqoAPg26VVBQgL+/P8aMGYM+ffpgwoQJMDY2RmpqKq5fvw4Oh4Pw8HAAgI+PD65cuYJhw4ZhypQp6NSpE969e4ewsDDs27cPbdu2xaJFi3D27FmMHz8e48aNg62tLT58+IAnT57g5MmTuH79Otq2bSvR94wQUn+UwBBCRMrPzw/Kyso4fvw49u3bB1tbWxw9ehSrV6/mKaehoYHQ0FCsWLECO3fuhLq6Ojw9PdG7d29MnjwZampqTNkvvvgCFy5cwObNm7Fnzx7k5eVBT08PPXr04LlqycDAABcvXsT69etx9OhR5OTkwMDAAM7OztDR0QFQeZVUeHg4tmzZguPHj+PgwYPQ0NCApaUlli1bBn19fcm8UYSQRmFlZ2dz6i5GCCGSERAQgJUrVyIuLg5GRkbSDocQIqMogSGESM2HDx947htTVFQER0dHlJeX4+7du1KMjBAi66gLiRAiNRMnToSJiQlsbGyQm5uLQ4cO4fnz59i1a5e0QyOEyDhKYAghUuPs7Iy9e/fi8OHDKC8vR8eOHREYGIhRo0ZJOzRCiIyjLiRCCCGEyB26DwwhhBBC5A4lMIQQQgiRO5TAEEIIIUTuUAJDCCGEELlDCQwhhBBC5A4lMIQQQgiRO5TAEEIIIUTuUAJDCCGEELlDCQwhhBBC5A4lMIQQQgiRO5TAEEIIIUTuUAJDCCGEELlDCQwhhBBC5A4lMIQQQgiRO5TAECIm6enpmDt3Lrp27YrWrVuDzWYjKSlJ5NuJiooCm82Gr6+vyNddk+DgYLFvMykpCWw2G3PnzhXbNsgn3M8Rvd9EXlAC00yx2Wyw2WxphyFRkv6BnjdvHvbv3w8bGxt8//33WLFiBbS1tYVe/tSpU8x+OnbsmBgjJaLg6+vL7C82m41WrVrBxMQEXbt2xahRo+Dn54dXr15JLT5ZSlBOnjyJZcuWYfDgwTA1NQWbzca0adNqLJ+bmwsvLy8MHjwYnTp1gr6+Ptq3bw9nZ2cEBASgoKBA6G1nZWUhKCgIEydORM+ePWFoaAhTU1O4uLhgx44dKC0tFbhcaWkptm3bBgcHBxgZGcHY2BguLi4ICgoCh8MRGPOSJUtgbW0NCwsLeHh41Lj/L168iNatW+Pff/8Vuh4EUJJ2AIQ0RSUlJYiIiICVlRX279/foHX8/fffPH+PGjVKRNERcfriiy/w5ZdfAgA+fPiAd+/e4c6dO7h8+TI2b96MuXPnYu3atVBUVJRypNKzefNmPHr0CBoaGjAyMkJeXl6t5d+/f49//vkHPXr0wMCBA9GmTRvk5uYiMjISK1euRFBQEM6dOyfUCcKJEyewZMkS6OrqwsHBAW5ubsjMzMSZM2fg7e2N0NBQnDhxAqqqqswyJSUlGDNmDCIjI2FiYgIPDw8oKSnh8uXLWLRoEaKjo7Ft2zae7cybNw/h4eEYOXIk2rRpg+DgYLi5ueHff/9Fy5YtmXK5ubn47rvvMHv2bNjb29fznWzeKIEhRAzS09NRUVEBPT29Bi2fnJyMS5cu4fPPPwdQefb88uVLtGvXTpRhEjH48ssv4e3tzTc9IiICCxYswLZt2/Dhwwf8+uuvUohONmzYsAHGxsZo164drl27BldX11rLm5iYIDk5GcrKynzzZs2ahUOHDmH37t34/vvv69y2paUlgoODMWjQIJ4kMjc3F0OHDsXNmzexa9cuLFiwgJm3e/duREZGolevXjhx4gQ0NDQAAMXFxRg3bhz27duHQYMGYdiwYQCAd+/eITw8HBMnToS/vz8AoGfPnpgzZw7OnTuHkSNHMutevXo1lJWVsXr16jpjJ7yoC4kwuGMOhg4dinfv3mH+/PmwsrKCkZERBg4ciBs3bgAACgoKsHr1anTt2hV6enqwt7fHiRMn+NZXdZzErVu34ObmBlNTU5iammLMmDGIiYnhWyY1NRUbN27E119/jQ4dOkBXVxedOnXC9OnT8eTJkxpjv3fvHqZNmwZra2vo6uqiQ4cOcHV1RUhICIDK5n3uj+T+/ft5mvqDg4OFen9iY2MxZcoUWFlZQVdXF126dMGCBQuQmJjIU87GxgY2NjYAgOvXrzPbqU/TfVBQECoqKjB+/HiMHz8eHA4H//zzj9DLc2VnZ2PdunXo27cvjIyMYGpqis8//xyrVq1CdnY2T9nExETMmzcPnTt3hq6uLqysrDBlyhQ8evSo1m3ExsZi7NixMDMzg6GhIYYMGVJjU3heXh7WrVuH3r17Q19fH2ZmZnB1dcWpU6fqXTdBOBwOgoKC8NVXX8HExASGhoZwcHCAv7+/wK4BGxsbsNlslJWV4ddff0WPHj2gp6eHLl26wMfHByUlJSKJCwD69++Po0ePQkVFBYGBgXjw4AFfmZcvX2LhwoXMd8vS0hITJkxo9Helvp//pKQkTJs2De3atYO+vj6cnJxw9uxZ0bwRABwdHWFpaQkWiyVUeUVFRYHJCwAMHz4cAJCQkCDUuvr164ehQ4fytYBpaWlh0aJFACpPGKoKCwsDACxdupRJXgBAVVUVq1atAgD8+eefzPSUlBQAwGeffcZM4/7NnQcAV69exd69e7F161aeVhkiHGqBIXxycnLw9ddfo1WrVnB3d8fbt29x8uRJjB49GhcuXMC3336L/Px8DBkyBHl5eTh69CimTp0KY2Nj9O7dm299d+/exZYtW9C/f3/MnDkTCQkJCAsLw/Xr13HixAmeZtMbN27g999/Z5p21dXVkZCQgNDQUJw5cwZnzpxB9+7dedYfFBSExYsXQ0FBAYMGDYKVlRUyMzPx4MED7NixA+PHj8eXX36J5ORk7N+/H127dsXQoUOZ5bnJRm0uXLiAb775BuXl5XB1dYWFhQUeP36Mffv2ITw8HKGhoejWrRsAYO7cuUhOTsYff/wBU1NTjB8/XujtAEBZWRmCg4PRsmVLjBgxAiwWC15eXggJCcGqVatq/CGvLjExEa6urkhJSUG3bt0wZcoUAJU/9Lt378bYsWOZcVAxMTEYPnw4cnNzMXDgQHTp0gWvXr1CWFgYzp49i5CQEDg7O/NtIyYmBlu3bkXv3r0xadIkvH79GqGhoRg+fDiioqJgZWXFlM3JycHgwYMRFxeHbt26Yc6cOcjJycGJEycwYcIEeHt7Y8WKFULVrSZz5szBwYMHYWRkhPHjx0NZWRlnz57F6tWrERERgUOHDkFJif9nb8aMGbh58ya++uoraGpq4sKFC/jf//6HjIwMBAQENCqmqjp16oQRI0bg0KFDOHr0KM9n+erVq5gwYQKKiorw9ddfw9LSEqmpqQgLC8PFixcREhICFxcXpnx9viv1+fynpKTAxcUF5ubm8PDwwPv373H8+HGMHz8eJ06cgKOjI095GxsbpKSkICwsDA4ODiJ7r4TFTay6du3a6HVxv1vVPyPv3r0DAJibm/MtY2FhAQC4efMmSktLoaysDBMTEwCVyT0XN2E1NTUFUHkiuHDhQkydOlUq71tTQAkM4fPo0SPMmjULGzduZM6Qfv31V/z8888YNmwYHBwcsGfPHqioqAAAnJ2dMXPmTPz+++8Cz+YuXryIzZs3Y+bMmcy0kydPYvLkyViwYAGio6OZ7Tg6OuL58+fQ1NTkWcfDhw8xaNAg/PTTTzh69Cgz/enTp1iyZAnU1dVx5swZdOnShWe5169fAwDzA8EdVCuoib8mBQUFmDNnDkpLS/l+wIOCgrBo0SLMmTMH169fB4vFwrx585CUlIQ//vgDZmZm9doWUPmDnJqaCk9PT+Z9cHNzw/79+3Hq1CmMGDFCqPXMmjULKSkpWLlyJZYvX84zLzs7m/mR5nA4TDIREBDAJFwAcOXKFYwcORKzZs1CbGws31niuXPnsH37dkyYMIGZ9tdff2Hx4sX4448/eLpJ1q5di7i4OEyYMAHbtm1j9vmyZcvg7OwMPz8/DBgwAD169BD+zari2LFjOHjwILp06YIzZ85AS0sLAODj44MxY8bg8uXL2LFjBxYuXMi37KtXr3Dr1i20atUKQGWz/pdffokDBw7Ax8cH+vr6DYpJkC+//BKHDh3CnTt3mGk5OTmYOnUqlJWVcfHiRXTq1ImZ9+zZM7i4uGD+/Pl48OABMzajPt+V+nz+r127Bi8vL3h5eTHT3N3dMXr0aPj7+/MlMJJUVlaGzZs3A6gcF3Pz5k08fPgQjo6OtQ4CFtbevXsBAF999RXPdB0dHSQkJCApKYln3wBgBuaWlpbi1atX6NChA/T19TFkyBD8888/yM/PR6tWrbB//36YmJhg4MCBAIA1a9agoqICa9asaXTczRV1IRE+6urq+PHHH3madz08PAB86pLgJi8AMGrUKCgrK+Phw4cC19euXTtMnz6dZ9rw4cNhZ2eH+Ph4nu4GXV1dvh9koPIsz8HBAdeuXePpCtizZw/KysqwdOlSvuQFAHMm1BinTp1CZmYm3Nzc+H68J02ahO7duyMuLg63b99u9LaAT4N3qyYS3ASh6sDe2sTExCA6OhqdO3fG0qVL+eaz2WymKfzff//F06dP0aNHD55tAoCTkxOGDRuG//77D6dPn+ZbT58+fXiSFwD45ptvoKSkhLt37zLTSktLceDAAbRs2RI//fQTz2fL2NgYS5YsYbp/Goq7rI+PD5O8AICKigo2bNgAADV2w61du5ZJXoDK74C7uzsqKipw//79BsckiJGREQAgMzOTmXbgwAFkZWVhxYoVfAfIjh07YtKkSUhLS8PVq1eZ6fX9rgjL1NQUy5Yt45nm4uICExMTnn3KFRoaiujoaPTs2bPe26qvsrIybNy4ERs3bsSff/6Jhw8fwtPTE8HBwVBTU2vUugMCAnDp0iV069YN33zzDc+8QYMGAQB++eUXniueSkpKmM8WAJ5u2R07dmDy5Mm4du0aDh8+jL59++LkyZNQV1fHjRs3sGfPHmzduhXq6upYt24drKys0KZNG3z99dc1/pYSXtQCQ/i0a9eOp58XAAwMDAAA2traaNu2Lc88RUVF6Orq4u3btwLX9/nnn0NBgT9X/uKLLxAdHY3Y2Fj06dOHmX7u3DkEBgYiJiYGmZmZKCsr41kuMzOTiYd7Flv9jEmUuE2/NZ15Ojk54cGDB3jw4AHs7Owata3k5GRcvnwZZmZmPM3KX3zxBczNzXH16lUkJiYKbMquiptMOTs7C3zvqxKmfmFhYXjw4AHGjBnDM8/W1pavvLKyMvT09Hh+zJ8/f47CwkL06tULOjo6ArdRNZaG4C4rqDm+a9eu0NXVxYsXL5Cfn8/3+RZUD27yW32sUGNxL7mtmsRxk/jHjx8LvLfOixcvAFS2xnDP4IH6fVeEZWNjI/AKKRMTE0RHR/NN53ahSIKamhqys7PB4XCQlpaGK1eu4KeffkL//v1x5MiROr8XNTl06BBWrVoFQ0ND7N27l6+bds6cOQgLC8Pt27fRp08fDBw4EAoKCoiIiMB///0HExMTvH79mue7pq2tjd9//51vWx8+fMDChQsxfvx4ODs7w9/fH7/++itWrlyJHj16wMfHB+7u7rh37x6Ni6kDJTCET9WzVy5ud4OgeUBlElP9x5OrpitxdHV1AVSO/ufasWMHvL29wWaz0b9/f5iYmKBFixZgsVg4deoUHj16hOLiYqZ8Tk4OgE9nteLAja+menC7F7ixNAZ38O64ceN4DnAsFgvjx4/Hhg0b8M8//8DHx6fW9XBjMTQ0rHObjalfTZetKioqory8XCTbEFZubi60tLTQokWLGreRkZGB3NxcvgRG0D2RuAfxqvUQhdTUVABAmzZtmGlZWVkAPnVh1KTq2X99vyvCqm2fVlRU1Ht94sBisWBoaIhx48bBysoKX331Fb7//nue7mVh7d+/H/Pnz4eRkRFCQ0P5TtAAoGXLljh16hR+//13nDhxAvv27YOamhocHBzg4+ODSZMmAfj0m1abdevWobCwEOvXrwcAbN++HU5OTkyrl7q6OgYNGoQjR44w6yWCUQJDxI47AK66jIwMAJ+SorKyMvj5+UFfXx9Xr17lO3MU1EXD/bF9+/at2G7Mx42vpnqkp6fzlGuosrIy7Nu3DwCYZnJBQkJCsHLlyloH83LfF+7BsjaSqJ+ktvH+/Xt8+PBBYBIjqv3UWNwrXHr16sVM48Z05coVga1B1TXku9JU9erVC9ra2rh+/To4HI7QVzYBlV2yixcvhpmZWY3JC1fLli2xcuVKrFy5kmf6hw8fkJCQgDZt2tS6PFC5X3bs2IHg4GBoa2sjJycHaWlpGDt2LFOGezHA06dPha5Hc0VjYIjY3bp1S+CZ2/Xr1wF8+sJmZmYiJycHdnZ2fD/I+fn5ArsXuAeBixcv1hlHQ8+ouVdyVL+0kisyMhKA4G6I+jhz5gzS0tJgYWGBiRMnCvzXoUMHpKenCxyPUhX3arDLly/XedZcV/244y4aU78OHTqgZcuWiIuL4xn7IcptcOtx7do1vnlxcXHIyMhA+/bt+VpfJOnp06c4efIkWCwWT3ccd3/dvHlTqPU05LsirhYlacvLy0NeXh4UFRXrlbzs2LED3333Hdq1a4fTp0/XmXzU5MiRIygpKYG7u3ut5YqLi7FgwQKMGTMGgwcPBvCpG7FqS5koL91v6iiBIWKXkJCAPXv28Ew7efIkoqOjYWVlxVxGrauri5YtWyImJgb5+flM2dLSUnh5eQk88E2fPh1KSkr45ZdfEBcXxzf/zZs3zN+tW7cG8OnKJGENHToUrVu3xsmTJ5mkiys4OBj379+HtbW1wEvI64M7QHfFihXw9/cX+O+HH34AUPNgVC5bW1vY29sjLi4Ov/zyC9/8nJwc5j22t7dHx44dcffuXRw8eJCn3NWrVxEWFgYdHR0MGTKkwXVTVlaGh4cHCgsLsXbtWp5br6empmLLli1gsVh8gyfrY+LEiQCAn376ie/zw33fpNkkf/XqVYwZMwYlJSWYOXMmz2W/33zzDdhsNjZv3ixwnAmHw8HNmzeZg1tDvisN/fzX5dWrV8wYJ3F5/PgxioqK+KaXlJRg2bJlqKio4BkbBACFhYV4/vy5wNv3//bbb/D29kanTp1w+vRpGBsb1xmDoO7N+/fv48cff0SbNm2wePHiWpf38/PD+/fv4efnx0zT0tKCkZERIiIimC748+fPAwDfYG7Cj7qQiNi5uLjghx9+wMWLF9GlSxfmPjAtWrSAv78/cxaioKCA2bNnY8uWLejbty+GDBmC0tJSREVF4f3793BwcOBrJejUqRN+/fVXLF68GE5OTsx9YN6/f4/Y2FgUFxczy1hZWcHExAQ3b97EzJkzYWlpCUVFRQwePLjWe0ioq6sjICAAkyZNwogRI+Dm5gZzc3M8evQI58+fh7a2Nnbs2FGvs7/qkpKSEBERATabXetl0kOGDIGuri4iIiKQlJRU61njzp07MWzYMGzYsAGnTp1iBre+evUKly9fxrlz59CtWzewWCzs2LEDI0aMwJw5c3D8+HHmPjChoaFQUVHBH3/80egBhT4+Prh58yaCgoIQGxsLJycn5j4w79+/x/Lly3m6Vepr9OjROHv2LA4fPow+ffpg6NChzH1gXrx4gX79+mHevHmNqoMwrl27xgzELS4uRnp6Om7fvo0XL15AUVERixYt4rt0tlWrVggKCsI333yDgQMHwtHREZ06dYKysjLevHmDO3fu4PXr10hMTISKikqDvisN/fzXxc3Nrd73gQkPD2duXsjtVrxz5w7PzR537NjB/L13716EhITA3t4epqam0NbWRmpqKiIiIpCeng5LS0tmTAnX3bt34erqClNTU56rekJCQpgr4RwcHBAYGMgXn7a2Nt9nxd7eHp07d0b79u3RsmVLPHnyBBcvXoSmpiZCQkJqvet2TEwM/P39sWfPHiaR5Jo/fz5++OEHuLq6omvXrti/fz+MjIzqbNEhlMAQCejVqxeWLVuG9evXM3er7N+/P1avXs3XZfDDDz9AR0cHe/fuxd9//w0tLS04OTlh1apVNT75ePLkyejcuTP8/f1x69YtnDlzBq1bt0bHjh0xY8YMppyioiL27duHNWvW4Ny5c8jLywOHw4GRkVGdP+CDBg3C+fPn8dtvv+Hq1as4efIkdHV1MW7cOCxfvrzBVz9wcQfvjh07ttbLQZWVlTFu3Dhs3boVQUFBtd5+3NzcHJGRkfD390d4eDh27doFVVVVmJiYYObMmTAzM2PK9ujRA1euXMHmzZtx5coVXLp0Cdra2hg6dCi+//57ppuvMdhsNs6dO4f//e9/CA0NRUBAAFRVVdGtWzfMnj0bbm5ujd7Gzp070bdvX+zduxd79+5FRUUFLC0t8dNPP2HOnDkCb2InatevX2fuCdSyZUuw2Wx06NAB7u7u8PDwqPGz4ujoiOvXr2Pbtm24dOkSoqOjoaSkBH19fdjZ2WHNmjU843fq+11pzOdf1B4+fMj3jLDk5GQkJyczr6smMCNGjEBBQQFu376N6Oho5OfnQ1NTE506dcL8+fMxffp0qKurC7Vt7hPhORwOdu3aJbCMqakpXwIzduxYXLx4Ebdv30ZJSQlMTEwwa9YsfPfdd7XeJ6i0tBTz5s3DkCFDmLsGVzV37lzk5OQgKCgI9+7dQ8+ePbF58+YaB6OTT1jZ2dn8j9EkRASCg4Mxf/58rFixot43cyOEEEJqQ2NgCCGEECJ3KIEhhBBCiNyhBIYQQgghcofGwBBCCCFE7lALDCGEEELkDiUwhBBCCJE7lMAQQgghRO5QAiOk+Ph4aYcgEVTPpoXq2bRQPZsWqmfj0J14CSHNTmJuKWZFZuO/4nK0UVXErn5stNWs+enehBDZQy0whJBmZ1ZkNqIzSvAytxzRGSWYeTVb2iERQuqJWmAIIc3Of8Xltb4m/KjVisgaaoEhhDQ7bVQVa31N+FGrFZE11AJTRVlZGQoKCgTOU1NTQ05OjoQjkrzmWE91dXWJPKWYyI5d/diYeZW3NYHUjlqtiKyhX+2PysrKkJeXBzabDRaLxTdfVVUVampqUohMsppbPTkcDrKzs6GpqUlJTDPSVlMZ54fpSjsMudJGVREvUc7zmhBpoi6kjwoKCmpMXkjTxWKxwGaza2x5I4RU2tWPDTtdFbTTUoSdrgq1WsmpxNxSDAzPQI+jaRgYnoGkvFJph9RgdMpZBSUvzRPtd0LqRq1WTQN3LBMAvEQ5Zl7Nltv9Si0whBBCSDPRlMYyUQJDCCGENBNN6Qo8SmCI0Dw8PDB37lzm9dChQ7Fs2bJGrXPu3Lnw8PBobGiESE1TGlNAmr6mNJaJxsCQBtu3b5/QV+5ERUXB1dUVCQkJ0NHRYab7+fmBw+GIK0RCxK4pjSkgTV9TGstECUwzU1JSAhUVFZGsq1WrVo1eh7a2tggiIUR6mtKYAkLkCXUhybmhQ4di8eLFWLFiBdq2bYu2bdti9erVqKioAADY2NjA19cX8+fPh5mZGWbOnAkA+PfffzFkyBAYGhrC2toaS5YsQW5uLrPewsJCzJ07F8bGxrCyssKvv/4qcNtVu5BKSkrw008/oWvXrtDT00P37t3xxx9/ICkpCa6urgAAS0tLsNlspiuqehdScXExvLy8YGVlBX19fXz11Ve4efMmMz8qKgpsNhtXr16Fi4sLDA0N4eTkhJiYGKZMTk4OZs2ahfbt20NfXx/du3dHQECACN5tQvg1pTEFhMgTaoGpg8Zkp8r/JbS9/H+u1HuZw4cPY9y4cbhw4QIeP36Mb7/9Fvr6+liwYAEAICAgAEuXLsWVK1fA4XDw+PFjjBo1Cl5eXvD398f79+/h7e2NBQsW4M8//wQArF69GleuXEFQUBAMDQ2xceNG3LhxA8OGDasxjrlz5+LmzZvw9fVF9+7dkZycjDdv3sDExARBQUGYNGkSbt26hVatWtV4s7wff/wRJ06cwLZt22Bubo7t27djzJgxuHv3LgwMDJhya9euxZo1a2BgYAAvLy/MmjUL//77L1gsFtatW4e4uDgcPHgQurq6SEpKQmZmZr3fV0KEQXf1JUQ6KIFpAvT19bFp0yawWCx06NABL168QEBAAJPA9O3bF99++y1Tfvbs2Rg5ciQWLlzITPv111/h6OiI9evXo1WrVti7dy+2bdsGFxcXAMD27dvRuXPnGmNISEjA0aNHceTIEXz11VcAAHNzc2Y+t7tJV1eXZwxMVQUFBQgMDMTWrVvx9ddfAwC2bNmCyMhI7N69G6tWrWLK/vDDD3B0dAQALF++HIMGDcLbt29hbGyMlJQUdO/eHT179gQAmJmZCfdGEtIATWlMASHyhLqQmoBevXrx3IzNzs4Ob9++ZbqEPvvsM57yDx48wKFDh2BsbMz8GzRoEAAgKSkJr169QklJCezs7JhlNDQ00KVLlxpjiI2NhYKCAhwcHBpcj1evXqG0tBR9+vRhpikqKsLOzg5Pnz7lKVs1Fm7LTEZGBgBg+vTpOH78OL744gusWrUK165da3BMhBBCZBO1wDQD6urqPK8rKiowadIkzJs3j69sq1at8Pr1a0mFJrTqd8tVVlbmm8e9mmnAgAF4+PAhLly4gKtXr8LDwwPDhw+ncTCEENKEUAJTB+6YlKKiIpl9yOHdu3fB4XCYA/nt27dhaGgILS0tgeW7d++OJ0+eoF27dnzzioqKYGFhAWVlZdy+fZvpBiooKEBcXBxPt1BVNjY2qKioQFRUFNOFVBX3yqfy8pqv0LCwsICKigpu3boFCwsLpnx0dDTGjBlT43KC6OjowNPTE56enhgwYACmT5+OLVu2QFVVtV7rIYQQIptkugvp+vXr8PT0hLW1NdhsNoKDg2ss+91334HNZsPf31+CEcqGtLQ0eHl5IT4+HidPnsTWrVsFtq5wffvtt7h37x4WL16MBw8e4OXLlzh79iy+++47AJXdRRMnTsSaNWsQERGBJ0+eYMGCBcyVTYK0b98eI0eOxKJFi3Dy5EkkJibixo0bOHDgAADA1NQULBYL586dw3///Yf8/Hy+dairq2PatGlYs2YNzp8/j2fPnmHJkiXIyMjAjBkzhH4/1q9fj/DwcCQkJODZs2cICwuDubk5JS+EENKEyHQLTEFBATp37oxx48Zhzpw5NZY7efIk7t69C0NDQwlGJzvc3d1RUVEBFxcXsFgsTJw4sdYEpmvXrjh9+jTWrVuHYcOGoby8HObm5hg6dChT5ueff0ZBQQG++eYbtGjRArNmzUJhYWGtcfzxxx9Yv349vLy8kJmZCSMjIyYOIyMjeHt7Y926dVi0aBE8PT2xY8cOvnWsXbsWADB//nzk5OSgW7duOHLkCM8VSHVRVVXFunXrkJSUBFVVVfTu3ZtJpAghhDQNrOzsbLm4DaqxsTE2bdqECRMm8ExPTk7G119/jRMnTmDMmDGYNWsWz9U1wsrJyan1pmqy2oU0dOhQdO7cGZs3bxbJ+mS1nqJWvZ517X95FR8fDysrK2mHIXZUz6aF6tm0iKueMt2FVJeysjLMmDEDS5cuRceOHaUdDiGEEEIkRKa7kOri6+uL1q1bY/r06fVaLj4+nm+amppanWMkioqK6rUdSaioqEBZWZlIY5PFeopD1Xrm5ubi3bt3UoxGfAR93psiqqf0vf7Awo/PVZBdygJbmYOfO5TAuEXDGvlluZ6iRPWsWV2tNnKbwERFRSEkJARRUVH1XlbQm5KTk1Nr14msdq2cOXNGpOuT1XqKWvV6amlpwdTUVIoRiQc1UTctsl7P+eEZeJhX+WDLlCJgQ7J2g27yJ+v1TMwtxaxI3rsvt9VUrnvBamS9nqJCXUjVXLt2DWlpaejYsSN0dHSgo6ODlJQU+Pj41HrHWEIIIeLRXB5syX0C+cvcckRnlGDm1Wy+Mom5pRgYnoEeR9MwMDwDSXmlkg+0iRNLC0xJSQkOHDiAqKgoZGRkYO3atejevTuys7Nx5swZODo6wtjYuFHbmDFjBoYPH84zbfTo0Rg9ejQmT57cqHUTQog0iOrMXlraqCriJcp5XjdFwiRq3CQHAF6iHDOvZtMjJ0RM5AlMVlYWXF1dERcXBz09PWRkZCA7OxtAZTP9+vXr8fTpU+Zy2drk5+fj5cuXACrHerx+/RqxsbFo1aoVTE1NoavL+2FQUlKCvr5+g5uqqt4MjjQf3Dv4EiJt8n7Qay4PthQmUWsurVHSJPIuJB8fH6SkpODs2bO4ceMGz8FBQUEBbm5uuHDhglDrun//PhwdHeHo6IgPHz7A19cXjo6O2LBhg6jDhrq6OrKzs+lg1sxwOBxkZ2fzPW6BEGmQ94Me98GW90Yb4PwwXblqPaqPXf3YsNNVQTstRdjpqghM1KonNU21NUqaRN4Cc/bsWcyePRv29vbIysrim29paYl9+/YJtS4HBwem9UYYDx8+FLpsdUpKStDU1GQegFhdbm5ujbfmb0qaYz01NTWhpCS349lJE9JcumDknTBPIG8urVHSJPJf7by8PJiYmNQ4v7i4uNbn4UiTkpJSjTcze/fuXZO8SqU6qich0kMHvaZDmCSHNI7IE5h27drh/v37NQ6kvXz5MqytrUW9WUIIkXt00CNEeCJPYCZPnozVq1ejb9++cHZ2BgCwWCwUFhZi06ZNuHz5crN84CIhpHmT9yuMSBPH4QAV5UBZKVBWBlZZ6ce/S8EqLQXKy4DSEv55ZaUot+wMjq7kn0Uo8gRm9uzZePr0KWbPng1NTU0AwLRp05CdnY3y8nLMmDGD73lGhBDS1Mn7FUZEBCoqeA78yrlZYKW/AcrLwPqYHKD8Y8LA83dplcTiY7mP66gzyai67urLfFwnPq6T1cCLWIpmrURZU0hgAGDLli3w9PTE8ePH8fLlS1RUVMDCwgIjR45E3759xbFJQgiRafJ+hZFc4HA+HsRLeQ/+5WU8iQCrSkLAf1Av4y1XJTHgm8ezzMckobQUrPLSajFUzmdVVPCE21VKb5PIlUnnJn1iu/TC3t4e9vb24lo9IYTIlSZzhVFFeeXBWdAZPPegzj2IVz+gV00mPk6rmkxwWxfaZmVCrWULJgmoLCdgO2XVl6e73UpFWZlUNkvXjhJCiAQIfYURh1Oly4C3a0At/TUUFMv5WgBQ9rGboJw/YWBxWyTq2/IgqNuitBQsToXguEWotdi3QAThKCgASsqAkjI4SsqAktLHv1WYvytfKwHKKoCiEjhKyuDoSb77CBBBAtOtW7d6372WxWIhJiamsZsmhJD643CA8vLKM/vqB36eA3e1s/46Wg14E47qAyHL0KmsBFFlVbo3ImtoXSiv+WyWrt+UfxxlZUBRGVBWRilYUFJrASgp8SQJn5IHlY/zlD8mDx/LKStXJg/KH5dRVP643o9JhrIyOB+3IXjdylXKfUxGlJQABflqFWx0AvPFF1/wJTAxMTF48uQJrK2tYWlpCQBISEhgptna2jZ2s4QQUiPFuHtQDdrC24UhgsGKRPZxFBWZBIEjMAmo1sKgrPLxIK78KRHgSRgELKNUdd0fy31skajcbtVluC0XKoCiIlDleNlcnkYtLo1OYHbs2MHzOjw8HKdPn0ZoaCgcHBx45l29ehWTJ0/GDz/80NjNEkJIzUpLoJCaIu0omiQOiyXggP4xSeC2LnAP6Er8LQA8rQDVWgC409P+y4S+iSnTulBzUsCbVEBJGVAQ+RNyiIwS+RiYDRs2YNasWXzJCwD069cPM2fOxPr16zF06FBRb5oQQirJ+aMhmBaBak3+xeUVUFFX/zS9hnJMd0LV1oXqycPHxGL1/QI8zGWhREERxQrK6NC6BQL66/K0RDBJgqIyXyuCOLyPj0cbapkgdRD5t/zly5dgs9k1zmez2Xj16pWoN0sIIQyOkkrt8xUU+FsNuOMGlKp0J1Q9069arkoSACWVWsp9ap2oNcmo2oKgqFRjgiCOLofQpDS8VPp0ddR/LRVRYWIg0m0QIg4iT2AsLCwQHByMSZMmQUNDg2deXl4egoODYW5uLurNEkIIo8LcCgW+/9TQQiF/gxXFqclc3k2aHZEnMD/88AMmT56M3r17w9PTExYWFgAqW2YOHjyIjIwM/P3336LeLCGEfKLaAhyjttKOQi7QAySJvBJ5AjNs2DAcPnwYPj4++P3333nm2djYYNu2bXBxcRH1ZgkhhDQAPUCSyCuxjHRzdnaGs7Mz0tPTkZJSeSWAqakp9PX1xbE5QgghhDQzYh2qr6+vT0kLIYQQQkSu0QnM9evXAVTe0K7q67pwyxNCCCGE1FejE5hhw4aBxWIhLS0NKioqzOuacDgcsFgsZGVlNXbThBBCCGmmGp3AhIWFAQBUVFR4XhNCCCGEiEujE5gvv/yy1teEEEIIIaIm8kG8BQUFyMrKgqmpqcD5KSkp0NHRQcuWLUW9aUIIaRYSc0sxK5L33i1tNZWlHRaRsub2uRD5U69WrlyJ8ePH1zh/woQJWL16tag3SwghzcasyGxEZ5TgZW45ojNKMPNqtrRDIg2UmFuKgeEZ6HE0DQPDM5CUV9rgdTW3z4XIE5iIiAgMGzasxvnDhg3DpUuXRL1ZQghpNv4rLq/1NZEfokw6mtvnQuQJTHp6OgwNDWucr6+vj7S0NFFvlhBCmo3qzyui5xfJL1EmHc3tcyHyBKZNmzZ4+vRpjfOfPn0KbW1tUW+WEEKajV392LDTVUE7LUXY6arQ84vkmCiTjub2uRD5IN4BAwbg77//hru7Oz777DOeeffu3cPff/+N0aNHi3qzhBDSbNDzi5oOUT5Ms7l9LkSewHh7e+PChQsYMGAABgwYAGtrawBAXFwcLl68CD09Pfzwww+i3iwhhBAikCxfndPckg5REnkCo6+vj4iICPj4+ODUqVM4e/YsAEBTUxNjx46Fj48PPR+JEEKIxHAHygLAS5Rj5tVsShqaALE8zFFPTw87duwAh8PBf//9B6BybExtjxgghBBCxKG5XZ3TXIj1adQsFgu6upTlEkIIkZ42qop4iXKe10T+NTqB2b9/PwDA09MTLBaLeV2XcePGNXbThMg1We6XJ6QpEeVAWSI7Gp3AzJs3DywWC6NHj4aKigrmzZtX5zIsFosSGDlCB1rxoH558aDPK6lO2IGy9NmRL41OYB48eADg09Ooua9J00EHWvGgfnnxoM+r7JPVRIE+O/Kl0QmMmZlZra+J/KMDrXjIar+8rB5chEWfV9knq4kCfXY+kYffAZHfiZc0Pc3t9tSSIqt3zZT3B8LR51X2yWqiQJ+dT+Thd0DkVyG5urrWWYbFYiE0NFTUmyZiQgPgxENWb2AlqwcXYdHnVfYJ0/oojRYA+ux8Ig+/AyJPYCoqKvju91JeXo6UlBS8efMG7dq1q/Vhj0T2yOqBloiHNLq2RHmwEuXnVR6a0eWRMImCNLqZ6LfuE1nt4q5K5AnMqVOnapx39uxZfPfdd1i/fr2oNyvTZPVHUFBchEjjLFRWx0TIalzyTphEQR5aAJoyeWiNEuuN7KobNGgQxo4dC29vb5w+fVqSm5YqWf0RFBTX9o5SDopInTTOQmX1YCWrcTUH8tACICnSOAmWh9YoiQ/itbCwwP3794Uqe/36dXh6esLa2hpsNhvBwcHMvNLSUvj4+KBv374wMjJCx44dMWPGDKSkpIgr9AaT1R9BWY2LND+yOnhSVuNqDmR1kLs0yMOAWmmQaAJTVlaG48ePQ0dHR6jyBQUF6Ny5M/z8/NCiRQueeYWFhXjw4AGWLl2Kq1evIiQkBG/evMGYMWNQVlYmjvAbTFZ/BGU1LtL8yOrBSlbjag64LQD3Rhvg/DBdmeh2lxY62RRM5F1I8+fPFzg9JycHd+7cQXp6utBjYAYOHIiBAwcCAN8dfrW1tXHixAmeaVu2bEGfPn3w7NkzdOnSpf7Bi4ms9iUKiqskLVvaYZFmSFabq2U1LtK8UHeaYCJPYCIjI/muQmKxWGCz2ejTpw8mTZoEZ2dnUW8WAJCXlwcAYLPZYll/Q8nqj6CguOLTpBQMIYQQgWT1JFjaRJ7APHz4UNSrFEpJSQlWrVqFQYMGwdjYWCoxEEIIIaImqyfB0sbKzs7miHKFN2/exKNHjzBz5kxm2tGjR+Hr64ucnByMHj0aGzZsgIJC/YbfGBsbY9OmTZgwYQLfvLKyMsyYMQNPnz7F6dOn0bp161rXFR8fX69tE9F5/YGFH5+rILuUBbYyBz93KIFxC5F+BMVK3uMnhBBREffvoZWVVa3zRd4Cs379eujq6jIJzIsXLzB37lyYm5vD1tYWf/75J8zMzIR6arUwysrKMH36dMTFxSE8PLzO5AWo+00RJD4+vkHLyRtx13N+eAYe5lVeup1SBGxI1pbKmUVD6ylM/LJ035+m9Lmt7X1tSvWsDdWzaZH3egr7ey6ueor8KqSnT5+iZ8+ezOsDBw5ATU0NFy9exOHDh+Hh4YF9+/aJZFulpaWYOnUqHj9+jLCwMOjr64tkvUR85H00vTDx0yWP4kHvKyGyRdq/5yJPYPLy8ngG0V66dAn9+/eHlpYWAODzzz9HcnKyUOvKz89HbGwsYmNjUVFRgdevXyM2NhYpKSkoKyvD5MmTcefOHezevRssFgvp6elIT0/Hhw8fRF0tIiLyfum2MPFL+0vdVOUUFsGw+D3AqWyipve1bom5pRgYnoEeR9MwMDwDSXml0g6JNCHS/j0XeReSgYEBnj17BgBITU1FbGwspkyZwszPzc2FkpJwm71//z7PwyF9fX3h6+uLcePGwcvLi7mbr5OTE89y27dvFzhWhkifvI+mFyZ+uuRRCGVlYOXnVP7LywHyc8DKzwUrr/I1d/qn/3PxuKgQAKD7xU68V9ag91UIsnoXcNI0SPv3XCxPo961axeKi4tx9+5dqKmpYciQIcz8R48eoW3btkKty8HBAdnZ2TXOr22ePJKlsRPiIu+j6YWJX9pfaomrKAfy8z4lG9UTEL5kJAeswoIGb+4z1QLk67Ru+u+rCFBrIBEnaf+eizyB8fb2xrt373Do0CFoaWkhICAAurqVFczNzUVYWBjPFUrkEzpbahqk/aVulIoKoDCvztaQqq9RmAcWR3JXYp3oq4IKKzl9fyWMWgNJUybyBEZdXR1//vmnwHkaGhqIi4tDy5YtRb3ZJoHOlsSj2T51m8OBYlEhWOmveRMQpmUkly9BQX4eWJwKaUcuEEdBARwNbbBKiqUditxoDq2BzaHlmggm0adRKygoQFtbW5KblCt0tiQeTeKp2xwOUFRYZwLCyssBqnTddKuQ0WSExQLUNcHR1AZHQ/vT/xra4GhogaPJrpxWZT5aqAP1vH9UcyfXrYFCopbr5kuiCQypXXM4WxKGqM+oZK5li8MBij/UmIB8ag2pNr9cth5SWhVHXbNKIqL16W9NwUkJWmoACpSgk8aTue83kRhKYGRIczhbEoaoz6jE3rJVXFSZjAgYxIqaBrOWyu7lrJyW6uCof0w6tNjVWki0eFtFNLTA0dACFOmnhEgHtVw3X/SrQ2SOsGdUwrbU1Oup2yXFvMmIoJaQ6q0mMjwmo1xFDSytypaPQjUtXC9QQ5qSBopbasO1qz5atWkFVEtQoETjB4j8oJbr5osSGCJzhD2jqrOlpqwUrPxcWOTn4JJlldaQyznIeZ0E1Uss/rEkRbJ7E0SOihrvmJFq3TWo3lWjoYX4xCTmFt5fh2cw7xcA/FOugvO9qcWPyDdquW6+KIEhMkfgGVVZGVgFuTw3PXN5lgznvFy0Kc2DTmkezOLy0eJOle6cDzXfa0RPctURiKOs/GnQapWxIjytIdXGkEBVrVHbpLEChJCmhBIYInkV5UBBXo2X9nbIy0Fk1S6aUzlgFebzreZnKYQuCEdRie9qGmhWaQ2pNm6Eo6kNqKgBLJZE46SxAoSQpoQSGNI4FRXAh4Lax4hwpxV8HNhaINkbn9UHR0Gh5tYQnqtsPnXVQK2lxJORhqCxAoSQpoQSGDkj1ps2MTc+e1Pn3VdZ+dz7jeTK7o3PWAqAhib/Zbwa2sgoKoGOhSV/y0lLDblIRhpCGmMF6CZjhBBxoQRGzgh9iTGHAxR9EOr5NKjSfdOtXDbHRXBYLKClZo3jQ3i6aqomIzXc+OxdfDy0Pw5uBT4eaCOy8V9xAR1oRYhuMkYIERdKYBpJImeYHA5QUgRWXg700l9hQHY2dErz0KY0D+3fFkA1s/xTEsJtFcnPAatMxu81osmGwJufCRjICnVNsd74jA604kEDhwkh4kIJTCM16MBXUiywiwaCxo5w/y+t3MYJQet7LtIq1RtHrWWNd1zlu8xXUxscdS1ASbY+enSgFQ8aOEwIERfZOorIof+Ky9G6NA/Gxe/RpjQPnfMKoHSJxdsSUr0Lp6RI2mHXqFxZFSzt6ndf5U1MUDVZ0dAClFWkHXaj0YFWPGjgMCFEXCiBaaQ2qor4/uUhzE69/GniXenFUxVHWeVjSwibd9yIulZlEiJgDEl8UjJz47PmRFYPtPI+CJZuMkYIERdKYBppVz82bj9iA6ni3Q5HSZkvCeFosQVc6vvpniONvfFZcyKrB1oam0MIIYJRAtNIbTWV0f4zQ+CJ8MtwFBUF3/SspruwamoDqi2a7OW9pGY0NocQQgSjBEYEKlrrodzI/FO3jLoWXwJS9Um+aKEu9mRE0l0P8t7VIatkeWwO7XNCiDRRAiMC5b374UPvftIOg4ekux6oq0M8ZHVsDkD7nBAiXZTANFGS7nqgrg7xEHZsjjRaQ2ifE0KkSfBtSoncq97VIO6uB0lvj/Ditoa8zC1HdEYJZl7NFvs2aZ8TQqSJEpgmalc/Nux0VdBOSxF2uipi73qQ9PYIL2m0htA+J4RIE3UhNVGSvixYVi9Dbi6kMdiX9jkhRJqoBYaQJoBaQwghzQ21wBDSBFBrCCGkuWFlZ2dzpB0EIYQQQkh9UBcSIYQQQuQOJTCEEEIIkTuUwBBCCCFE7lACQwghhBC5QwkMIYQQQuQOJTDVvH//HsuWLUPv3r1hYGCALl26YMmSJcjKyqpz2ZMnT8Le3h56enqwt7dHWFiYBCJuuL///hvDhg2DmZkZ2Gw2kpKS6lwmODgYbDab719RUZEEIm6YhtQTkL/9WVxcjGXLlqFdu3YwMjKCp6cn3rx5U+syvr6+fPuyQ4cOEopYeLt370a3bt2gr6+Pfv364caNG7WWv3btGvr16wd9fX10794dgYGBEoq0cepTz6ioKIHfxefPn0sw4vq7fv06PD09YW1tDTabjeDg4DqXefz4MYYMGQIDAwNYW1tj48aN4HBk+wLa+tYzKSlJ4P68ePGihCKuv99++w39+/eHqakpLC0t4eHhgbi4uDqXE9X+pASmmtTUVKSmpmLt2rW4ceMGdu7ciRs3bmD69Om1LhcdHY1p06bB3d0dUVFRcHd3x5QpU3Dnzh0JRV5/hYWFcHZ2hpeXV72Wa9myJZ49e8bzT01NTUxRNl5D6imP+9Pb2xthYWHYs2cPTp8+jby8PHh4eKC8vPbHClhZWfHsy7qSA0k7duwYvLy88P333yMyMhJ2dnZwd3dHSkqKwPKJiYkYO3Ys7OzsEBkZiSVLlmD58uU4efKkhCOvn/rWk+vWrVs8+8/S0lJCETdMQUEBOnfuDD8/P7Ro0aLO8rm5uRg5ciT09PRw+fJl+Pn5wd/fH9u2bZNAtA1X33pyHT16lGd/Ojo6ijHKxrl27RqmT5+Oc+fOITQ0FEpKShgxYgTev39f4zKi3J90HxghnD9/Hh4eHkhKSoKWlpbAMlOnTsX79+9x4sQJZtrw4cPRpk0b7NmzR0KRNsz9+/fRv39/PHjwAG3btq21bHBwMJYvX17nmb0sqk895W1/5uTkoH379ti+fTvGjh0LAHj9+jVsbGxw5MgRuLi4CFzO19cXoaGhuHnzpiTDrRcXFxd06dIFW7duZab16NEDw4cPh4+PD195Hx8fhIWF4d69e8y0hQsX4unTp7hw4YJEYm6I+tYzKioKrq6uSEhIgI6OjiRDFRljY2Ns2rQJEyZMqLHMnj17sGbNGjx//pxJBDZv3ozAwEDExcWBxWJJKtwGE6aeSUlJ6N69OyIiIvDZZ59JMDrRyc/Ph5mZGYKDgzF48GCBZUS5P6kFRgh5eXlQVVVFy5Ytayxz+/ZtODs780xzcXHBv//+K+7wJO7Dhw/o2rUrOnfuDA8PDzx48EDaIYmcvO3PmJgYlJaW8sRsYmKCjh071hlzYmIiOnXqhG7dumHatGlITEwUc7TCKykpQUxMDN++cHZ2rrFe0dHRAvfd/fv3UVpaKrZYG6Mh9eRycnJCx44d4ebmhsjISHGGKRXR0dH4/PPPeVoxXFxckJqaKnR3sDyZOHEi2rdvj6+//lrmWw2ry8/PR0VFBdhsdo1lRLk/KYGpQ3Z2NtavX49JkyZBSanmJy+kp6dDV5f3Vu66urp49+6duEOUKCsrK2zbtg0hISHYvXs3VFVVMWjQICQkJEg7NJGSt/357t07KCoq8p2J1xVzr169EBAQgCNHjmDr1q1IT0/HwIEDhRrzJQmZmZkoLy+v17549+6dwPJlZWXIzMwUW6yN0ZB6GhgY4LfffsPevXuxd+9eWFlZYfjw4TLXBdhYNe1P7rymQkNDAz///DP++usvHD58GI6Ojpg6dSoOHjwo7dCE5uXlBRsbG9jZ2dVYRpT7s9k8C2ndunX45Zdfai0TFhYGBwcH5nV+fj7GjRsHQ0ND/PTTT+IOUSQaUs/6sLOz4/lw2tvbw8HBATt37sSmTZsatM6GEHc9ZYWw9WyoAQMG8Lzu1asXbG1tERISggULFjR4vUT8rKysYGVlxby2s7NDcnIytm7dir59+0oxMtIQOjo6WLhwIfP6s88+Q1ZWFv73v//Bw8NDipEJZ+XKlbh16xbOnj0LRUVFiWyz2SQwc+fOZcYG1MTExIT5Oz8/H+7u7gCAgwcP1jlIVV9fHxkZGTzTMjIyoKen18CIG6a+9WwsRUVF2Nra4uXLlyJbpzDEXU9525+3b99GeXk5MjMz0aZNG2ZeRkYGPv/8c6G3p6GhgU6dOkl8f9ZER0cHioqK9doXenp6AssrKSnJ7FiRhtRTkJ49e+LYsWOiDk+qatqf3HlNWc+ePYW6SkvavL29cezYMYSFhcHc3LzWsqLcn80mgdHR0RH6xysvLw/u7u7gcDg4cuQINDQ06lymd+/eiIiIwKJFi5hpERERsLe3b3DMDVGfeooCh8PB48eP0bVrV4ltExB/PeVtf9ra2kJZWRkRERFM4v3mzRs8e/asXjEXFRUhPj5eZlquVFRUYGtri4iICIwYMYKZHhERATc3N4HL2NnZITw8nGcad2CksrKyOMNtsIbUU5CHDx9CX19fDBFKj52dHdasWYOioiLmRDIiIgKGhoZ1DsaXd/KwP1esWIHjx48jLCxMqFswiHJ/0hiYavLy8jBq1ChkZ2cjICAAhYWFSE9PR3p6OkpKSphybm5uWLt2LfN6zpw5iIyMxJYtW/D8+XP89ttviIqKwty5c6VRDaGkp6cjNjYWL168AAA8e/YMsbGxPJfAVa+nn58fLl26hMTERMTGxmLBggV4/Pgxpk2bJvH4hdWQesrb/tTW1sbEiRPh4+ODK1eu4MGDB5g9eza6dOkCJycnplzv3r3x559/Mq9XrVqFa9euITExEXfu3MHkyZNRWFiIcePGSaEWgs2fPx8hISEICgrCs2fPsGLFCqSlpWHq1KkAgNmzZ2P27NlM+alTpyI1NRVeXl549uwZgoKC5KJLrL71DAgIQHh4OBISEvDkyROsXbsWp06dwsyZM6VVBaHk5+cjNjYWsbGxqKiowOvXrxEbG8tcLr527VqepG3MmDFo0aIF5s2bh7i4OISGhuL333/HvHnzZPoKpPrWMyQkBIcPH8azZ88QHx8Pf39/7N69G7NmzZJWFeq0dOlShISEYNeuXWCz2cyxMj8/nykjzv3ZbFpghBUTE4Pbt28DqGy+q6rqmIpXr17B2NiYmWdvb4/AwECsW7cOGzZsgIWFBQIDA9GrVy/JBV9PgYGB2LhxI/Oa21Wxfft25nK/6vXMycnBt99+i3fv3kFLSwvdunXD6dOn+d4rWdKQesrj/vT19YWioiKmTp2KoqIiODo64o8//uDpj46Pj+cZyPr27VvMmDGD6Xrq1asXLly4ADMzM2lUQaBRo0YhKysLmzdvRnp6OqytrXHo0CEmxtevX/OUNzc3x6FDh7By5UoEBgbCwMAAGzduxPDhw6URvtDqW8/S0lL8+OOPePv2LdTU1JjyAwcOlEb4Qrt//z5cXV2Z176+vvD19cW4ceOwY8cOpKWl4dWrV8x8bW1tHD9+HEuXLkX//v3BZrMxf/58mU9I61tPAPjll1+QkpICRUVFWFpaYtu2bTI9/mX37t0AwPfdWrFiBby9vQFArPuT7gNDCCGEELlDXUiEEEIIkTuUwBBCCCFE7lACQwghhBC5QwkMIYQQQuQOJTCEEEIIkTuUwBBCCCFE7lACQwghNWCz2fD19WVeR0VFgc1mIyoqSopREUIASmAIIYQQIocogSGEEEKI3KEEhhBCCCFyhxIYQohIJScnY+nSpbCzs4OhoSHMzMzg4eGBx48fCyzr6ekJIyMjtG/fHl5eXrh06ZLAcSb37t2Du7s7zMzMYGBggEGDBiEyMpJvnWlpafjuu+/QuXNn6OnpwcbGBosWLUJeXh5TJicnBytXrkTXrl2hq6uL7t27Y9OmTSgvLxf9G0IIEQt6mCMhRKTu37+PGzduwM3NDaampkhNTcXff/+NoUOH4tatWzAwMAAAFBQUwM3NDWlpaZgzZw4MDAxw+PBhgQNkr127htGjR8PGxgbLli2DsrIyDh48iFGjRuH48ePMQ1bT09Ph4uKCzMxMTJ48GdbW1khNTUV4eDiysrKgqamJDx8+wNXVFcnJyZg2bRrMzMxw9+5d+Pn5ISUlBf7+/hJ9vwghDUMJDCFEpAYOHMj3dFpPT0/Y29tj7969WLZsGQDgr7/+QmJiIv755x+m/JQpU+Do6MizLIfDweLFi9GnTx+cOHECLBYLADBt2jQ4Ojri559/xvnz5wEAa9asQWpqKs6fP8/z5HBvb29wOJXPrQ0ICEB8fDyuXr2KDh06MNtt27Yt1q1bh0WLFsHKykoM7wwhRJSoC4kQIlItWrRg/i4sLGRaPtq3b4+YmBhm3qVLl6Cvrw83NzdmmpqaGiZNmsSzvocPHyI+Ph5jxoxBVlYWMjMzkZmZiby8PDg5OeHOnTsoLCxERUUFTp06hQEDBvAkL1zcxOf48ePo06cPdHR0mHVlZmbCyckJQGVrDyFE9lELDCFEpIqKirBhwwYcOnQIaWlpPPNat27N/J2SkgJzc3MmseBq164dz+uEhAQAwMKFC7Fw4UKB28zKyoKKigpyc3NhbW1da3wJCQl49OgRLC0tBc7PyMiodXlCiGygBIYQIlLLly/Hvn37MGvWLNjb20NbWxsKCgrw9vZGRUVFvdfHXWbNmjWwtbUVWKZNmzbIzc0Ven2Ojo5YsmSJwPnm5ub1jpEQInmUwBBCROrEiRPw9PSEn58fz/Ts7GyeFhhTU1PExcWBw+HwtMK8fPmSZzkLCwsAgIaGBtPNI4iKigq0tLTw5MmTWuOzsLBAfn5+resihMg+GgNDCBEpRUVFZsAs15EjR5CamsozzdnZGenp6QgNDWWmFRUVISgoiKecra0t2rVrh+3bt/NcCs3133//AQAUFBQwdOhQXLhwAXfu3OErx41p5MiRuHfvHjPwt6q8vDwUFxcLWVNCiDRRCwwhRKQGDx6MAwcOQFNTE507d8bDhw9x7Ngxvq6ZqVOnYteuXZgzZw7u37/PXEatqqoK4NOgWwUFBfj7+2PMmDHo06cPJkyYAGNjY6SmpuL69evgcDgIDw8HAPj4+ODKlSsYNmwYpkyZgk6dOuHdu3cICwvDvn370LZtWyxatAhnz57F+PHjMW7cONja2uLDhw948uQJTp48ievXr6Nt27YSfc8IIfVHCQwhRKT8/PygrKyM48ePY9++fbC1tcXRo0exevVqnnIaGhoIDQ3FihUrsHPnTqirq8PT0xO9e/fG5MmToaamxpT94osvcOHCBWzevBl79uxBXl4e9PT00KNHD56rlgwMDHDx4kWsX78eR48eRU5ODgwMDODs7AwdHR0AlVdJhYeHY8uWLTh+/DgOHjwIDQ0NWFpaYtmyZdDX15fMG0UIaRRWdnY2p+5ihBAiGQEBAVi5ciXi4uJgZGQk7XAIITKKEhhCiNR8+PCB574xRUVFcHR0RHl5Oe7evSvFyAghso66kAghUjNx4kSYmJjAxsYGubm5OHToEJ4/f45du3ZJOzRCiIyjBIYQIjXOzs7Yu3cvDh8+jPLycnTs2BGBgYEYNWqUtEMjhMg46kIihBBCiNyh+8AQQgghRO5QAkMIIYQQuUMJDCGEEELkDiUwhBBCCJE7lMAQQgghRO5QAkMIIYQQufN/2qF7lPqOXh4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "weights = kernel(drinking[\"agecell\"], c=0, h=1)\n",
    "\n",
    "for p, cause in enumerate([\"all\", \"mva\", \"suicide\"], 1):\n",
    "    ax = plt.subplot(3,1,p)\n",
    "    drinking.plot.scatter(x=\"agecell\", y=cause, ax=ax)\n",
    "    m = smf.wls(f\"{cause}~agecell*threshold\", rdd_df, weights=weights).fit()\n",
    "    ate_pct = 100*((m.params[\"threshold\"] + m.params[\"Intercept\"])/m.params[\"Intercept\"] - 1)\n",
    "    drinking.assign(predictions=m.fittedvalues).plot(x=\"agecell\", y=\"predictions\", ax=ax, color=\"C1\")\n",
    "    plt.title(f\"Impact of Alcohol on Death: {np.round(ate_pct, 2)}%\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the exception of suicide, it looks like adding the kernel weight made the negative impact on alcohol bigger. Once again, if we want to minimize the death rate, we should NOT recommend lowering the legal drinking age, since there clear impact of alcohol on the death rate.\n",
    "\n",
    "This simple case covers what happens when regression discontinuity design works perfectly. Next, we will see some diagnostics that we should run in order to check how much we can trust RDD and talk about a topic that is very dear to our heart: the effect of education on earnings.\n",
    "\n",
    "## Sheepskin Effect and Fuzzy RDD\n",
    "\n",
    "When it comes to the effect of education on earnings, there are two major views in economics. The first one is the widely known argument that education increases human capital, increasing productivity and thus, earnings. In this view, education actually changes you for the better. Another view is that education is simply a signaling mechanism. It just puts you through all these hard tests and academic tasks and, if you can make it, it signals to the market that you are a good employee. In this view, education doesn't make you more productive. It only tells the market how productive you have always been. What matters here is the diploma. If you have it, you will be paid more. We refer to this as the **sheepskin effect**, since diplomas were printed in sheepskin in the past.\n",
    "\n",
    "To test this hypothesis, [Clark and Martorell](https://faculty.smu.edu/millimet/classes/eco7321/papers/clark%20martorell%202014.pdf) used regression discontinuity to measure the effect of graduating 12th grade on earnings. In order to do that, they had to think about some running variable where students that fall above it graduate and those who fall below it, don't. They found such data in the Texas education system.\n",
    "\n",
    "In order to graduate in Texas, one has to pass an exam. Testing starts at 10th grade and students can do it multiple times, but eventually, they face a last chance exam at the end of 12th grade. The idea was to get data from students who took those last chance exams and compare those that had barely failed it to those that barely passed it. These students will have very similar human capital, but different signaling credentials. Namely, those that barely passed it, will receive a diploma. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avgearnings</th>\n",
       "      <th>minscore</th>\n",
       "      <th>receivehsd</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11845.086</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9205.679</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8407.745</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11114.087</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10814.624</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0.306667</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avgearnings  minscore  receivehsd   n\n",
       "0    11845.086     -30.0    0.416667  12\n",
       "1     9205.679     -29.0    0.387097  31\n",
       "2     8407.745     -28.0    0.318182  44\n",
       "3    11114.087     -27.0    0.377778  45\n",
       "4    10814.624     -26.0    0.306667  75"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheepskin = pd.read_csv(\"./data/sheepskin.csv\")[[\"avgearnings\", \"minscore\", \"receivehsd\", \"n\"]]\n",
    "sheepskin.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One again, this data is grouped by the running variable. It contains not only the running variable (`minscore`, already centered at zero) and the outcome (`avgearnings`), but it also has the probability of receiving a diploma in that score cell and the size of the call (`n`). So, for example, out of the 12 students in the cell -30 below the score threshold, only 5 were able to get the diploma. \n",
    "\n",
    "This means that there is some slippage in the treatment assignment. Some students that are below the passing threshold managed to get the diploma anyway. Here, the regression discontinuity is **fuzzy**, rather than sharp. Notice how the probability of getting the diploma doesn't jump from zero to one at the threshold. But it does jump from something like 50% to 90%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheepskin.plot.scatter(x=\"minscore\", y=\"receivehsd\", figsize=(10,5))\n",
    "plt.xlabel(\"Test Scores Relative to Cutoff\")\n",
    "plt.ylabel(\"Fraction Receiving Diplomas\")\n",
    "plt.title(\"Last-chance Exams\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can think of fuzzy RD as a sort of non compliance. Passing the threshold should make everyone receive the diploma, but some students, the never takers, don't get it. Likewise, being below the threshold should prevent you from getting a diploma, but some students, the allways takers, manage to get it anyway. \n",
    "\n",
    "Just like we have the potential outcome, we have the potential treatment status in this situation. \\\\(T_1\\\\) is the treatment everyone would have received had they been above the threshold. \\\\(T_0\\\\) is the treatment everyone would have received had they been below the threshold. As you've might have noticed, we can think of the **threshold as an Instrumental Variable**. Just as in IV, this non compliance makes the treatment effect we can naively estimate biased to zero. \n",
    "\n",
    "![img](./data/img/rdd/rdd_fuzzy.png)\n",
    "\n",
    "The probability of treatment being less than one, even above the threshold, makes the outcome we observe less than the true potential outcome \\\\(Y_1\\\\). By the same token, the outcome we observe below the threshold is higher than the true potential outcome \\\\(Y_0\\\\). This makes it look like the treatment effect at the threshold is smaller than it actually is and we will have to use IV techniques to correct for that.\n",
    "\n",
    "\n",
    "Just like we've assumed smoothness on the potential outcome, we now assume it for the potential treatment. Also, we need to assume monotonicity, just like in IV. In case you don't remember, it states that \\\\(T_{i1}>T_{i0} \\ \\forall i\\\\). This means that crossing the threshold from the left to the right only increases your chance of getting a diploma (or that there are no defiers). With these 2 assumptions, we have a Wald Estimator for LATE.\n",
    "\n",
    "\n",
    "$$\n",
    "\\dfrac{\\lim_{r \\to c^+} E[Y_i|R_i=r] - \\lim_{r \\to c^-} E[Y_i|R_i=r]}{\\lim_{r \\to c^+} E[T_i|R_i=r] - \\lim_{r \\to c^-} E[T_i|R_i=r]} = E[Y_{1i} - Y_{0i} | T_{1i} > T_{0i}, R_i=c]\n",
    "$$\n",
    "\n",
    "Notice how this is a local estimate in two senses. First, it is local because it only gives the treatment effect at the threshold \\\\(c\\\\). This is the RD locality. Second, it is local because it only estimates the treatment effect for the compliers. This is the IV locality.\n",
    "\n",
    "To estimate this, we will use 2 linear regression. The numerator can be estimated just like we've done before. To get the denominator, we simply replace the outcome with the treatment. But first, let's talk about a sanity check we need to run to make sure we can trust our RDD estimates.\n",
    "\n",
    "### The McCrary Test\n",
    "\n",
    "\n",
    "One thing that could break our RDD argument is if people manipulate where they stand at the threshold. In the sheepskin example this could happen if students just below the threshold found a way around the system to increase their test score by just a bit. Another example is when you need to be below a certain income level to get a government benefit. Some families might lower their income on purpose, just to be just eligible for the program.\n",
    "\n",
    "In these sorts of situations, we tend to see a phenomenon called bunching on the density of the running variable. This means that we will have a lot of entities just above or just below the threshold. To check this, we can plot the density function of the running variable and see if there are any spikes around the threshold. For our case, the density is given by the `n` column in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "ax = plt.subplot(2,1,1)\n",
    "sheepskin.plot.bar(x=\"minscore\", y=\"n\", ax=ax)\n",
    "plt.title(\"McCrary Test\")\n",
    "plt.ylabel(\"Smoothness at the Threshold\")\n",
    "\n",
    "ax = plt.subplot(2,1,2, sharex=ax)\n",
    "sheepskin.replace({1877:1977, 1874:2277}).plot.bar(x=\"minscore\", y=\"n\", ax=ax)\n",
    "plt.xlabel(\"Test Scores Relative to Cutoff\")\n",
    "plt.ylabel(\"Spike at the Threshold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first plot shows how our data density looks like. As we can see, there are no spikes around the threshold, meaning there is no bunching. Students are not manipulating where they fall on the threshold. Just for illustrative purposes, the second plot shows what bunching would look like if students could manipulate where they fall on the threshold. We would see a spike in the density for the cells just above the threshold, since many students would be there. Getting this out of the way, we can go back to estimate the sheepskin effect.\n",
    "\n",
    "As I've said before, the numerator of the Wald estimator can be estimated just like we did in the Sharp RD. Here, we will use as weight the kernel with a bandwidth of 15. Since we also have the cell size, we will multiply the kernel by the sample size to get a final weight for the cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td> 1.399e+04</td> <td>   83.678</td> <td>  167.181</td> <td> 0.000</td> <td> 1.38e+04</td> <td> 1.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>minscore</th>           <td>  181.6636</td> <td>   16.389</td> <td>   11.084</td> <td> 0.000</td> <td>  148.588</td> <td>  214.739</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>threshold</th>          <td>  -97.7571</td> <td>  145.723</td> <td>   -0.671</td> <td> 0.506</td> <td> -391.839</td> <td>  196.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>minscore:threshold</th> <td>   18.1955</td> <td>   30.311</td> <td>    0.600</td> <td> 0.552</td> <td>  -42.975</td> <td>   79.366</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheepsking_rdd = sheepskin.assign(threshold=(sheepskin[\"minscore\"]>0).astype(int))\n",
    "model = smf.wls(\"avgearnings~minscore*threshold\",\n",
    "                sheepsking_rdd,\n",
    "                weights=kernel(sheepsking_rdd[\"minscore\"], c=0, h=15)*sheepsking_rdd[\"n\"]).fit()\n",
    "\n",
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is telling us that the effect of a diploma is -97.7571, but this is not statistically significant (P-value of 0.5). If we plot these results, we get a very continuous line at the threshold. More educated people indeed make more money, but there isn't a jump at the point where they receive the 12th grade diploma. This is an argument in favor of the view that says that education increases earnings by making people more productive, since there is no sheepskin effect. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sheepskin.plot.scatter(x=\"minscore\", y=\"avgearnings\", color=\"C0\")\n",
    "sheepskin.assign(predictions=model.fittedvalues).plot(x=\"minscore\", y=\"predictions\", ax=ax, color=\"C1\", figsize=(8,5))\n",
    "plt.xlabel(\"Test Scores Relative to Cutoff\")\n",
    "plt.ylabel(\"Average Earnings\")\n",
    "plt.title(\"Last-chance Exams\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, as we know from the way non compliance bias works, this result is biased towards zero. To correct for this, we need to scale it by the first stage and get the Wald estimator. Unfortunately, there isn't a good Python implementation for this, so we will have to do it manually and use bootstrap to get the standard errors.\n",
    "\n",
    "The code below rund the numerator of the Wald estimator just like we did before and it also constructs the denominator by replacing the target variable with the treatment variable `receivehsd`. The final step just divides the numerator by the denominator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wald_rdd(data):\n",
    "    weights=kernel(data[\"minscore\"], c=0, h=15)*data[\"n\"]\n",
    "    denominator = smf.wls(\"receivehsd~minscore*threshold\", data, weights=weights).fit()\n",
    "    numerator = smf.wls(\"avgearnings~minscore*threshold\", data, weights=weights).fit()\n",
    "    return numerator.params[\"threshold\"]/denominator.params[\"threshold\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed \n",
    "\n",
    "np.random.seed(45)\n",
    "bootstrap_sample = 1000\n",
    "ates = Parallel(n_jobs=4)(delayed(wald_rdd)(sheepsking_rdd.sample(frac=1, replace=True))\n",
    "                          for _ in range(bootstrap_sample))\n",
    "ates = np.array(ates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the bootstrap samples, we can plot the distribution of ATEs and see where the 95% confidence interval is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ates, kde=False)\n",
    "plt.vlines(np.percentile(ates, 2.5), 0, 100, linestyles=\"dotted\")\n",
    "plt.vlines(np.percentile(ates, 97.5), 0, 100, linestyles=\"dotted\", label=\"95% CI\")\n",
    "plt.title(\"ATE Bootstrap Distribution\")\n",
    "plt.xlim([-10000, 10000])\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, even when we scale the effect by the first stage, it is still not statistically different from zero. This means that education doesn't increase earnings by a simple sheepskin effect, but rather by increasing one's productivity.\n",
    "\n",
    "## Key Ideas\n",
    "\n",
    "We learned how to take advantage of artificial discontinuities to estimate the causal effect. The idea is that we will have some artificial threshold that makes the probability of treatment jump. One example that we saw was how age makes the probability of drinking jump at 21 how we can use this to estimate the impact of drinking. We use the fact that very close to the threshold, we have something close to a randomized trial. Entities very close to the threshold could have gone either way and what determines where they've landed is essentially random. With this, we can compare those just above and just below to get the treatment effect. We saw how we could do that with weighted linear regression using a kernel and how this even gave us, for free, standard errors for our ATE.\n",
    "\n",
    "Then, we look at what would happen in the fuzzy RD design, where we have non compliance. We saw how we could approach the situation much like we did with IV.\n",
    "\n",
    "\n",
    "## References\n",
    "\n",
    "I like to think of this entire book as a tribute to Joshua Angrist, Alberto Abadie and Christopher Walters for their amazing Econometrics class. Most of the ideas here are taken from their classes at the American Economic Association. Watching them is what is keeping me sane during this tough year of 2020.\n",
    "* [Cross-Section Econometrics](https://www.aeaweb.org/conference/cont-ed/2017-webcasts)\n",
    "* [Mastering Mostly Harmless Econometrics](https://www.aeaweb.org/conference/cont-ed/2020-webcasts)\n",
    "\n",
    "I'll also like to reference the amazing books from Angrist. They have shown me that Econometrics, or 'Metrics as they call it, is not only extremely useful but also profoundly fun.\n",
    "\n",
    "* [Mostly Harmless Econometrics](https://www.mostlyharmlesseconometrics.com/)\n",
    "* [Mastering 'Metrics](https://www.masteringmetrics.com/)\n",
    "\n",
    "Other important reference is Miguel Hernan and Jamie Robins' book. It has been my trustworthy companion in the most thorny causal questions I had to answer.\n",
    "\n",
    "* [Causal Inference Book](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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